dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank the  Crick Advanced Sequencing Facility , especially  Robert Goldstone  and  Amelia Edwards  for their excellent support. We thank  Debipriya Das  and  Ana Agua-Doce  from the  Crick Flow Cytometry Facility  for technical assistance, and the  Crick animal facility  for animal care. We thank the members of the  Crick Digital Development Team , particularly  Amy Strange ,  Luke Nightingale ,  Jude Pinnock  and  Marc Pollitt  for excellent technical support. We thank the  Harvard Neurobiology Imaging Facility  for consultation and RNAscope services that supported this work. This facility is supported in part by the  Neural Imaging Center  as part of an  NINDS   P30  Core Center grant # NS072030. We thank  Keeley Baker ,  Gilad Barnea ,  Bob Datta ,  Kevin Franks  and  Paul Greer  for critical comments on the manuscript.
Data and code availability Raw single nucleus RNA and bulk RNA deep sequencing data have been deposited in Gene Expression Omnibus (GEO) under the accession numbers GSE162654 and GSE162655 respectively. The R and Python analysis scripts developed for this paper are available at the GitLab links https://gitlab. com/fleischmann-lab/molecular-characterization-of-projectionneuron-subtypes-in-the-mouse-olfactory-bulb and https://gitlab. inria. fr/acrombac/projectionneurons-mouse-olfactory-bulb. Extensive computational tools for additional in-depth exploration of our data sets are available through our website: https://biologic. crick. ac. uk/OB_projection_neurons.
Images were acquired with an upright Leica SP8 DIVE (Leica microsystems Gmbh, Wetzlar, Germany) coupled with a Coherent Discovery (Coherent Inc. , Santa Clara, CA, USA) pulsed laser and controlled by LAX imaging software. Both 552 nm continuous laser and 1040 nm pulsed laser were used to acquire tdTomato fluorescence and second harmonic generation, respectively, through a ×25 objective in PBS immersion. Because of the intrinsic high-penetration depth (~100 µm) of two-photon microscopy techniques, SHG can be used to image the orientation of collagen fibers in the relevant optical sections, without the need to actually perform physical sectioning of the sample. In contrast, antibody staining of collagen is most often performed on tissue sections, on which it is very difficult to assess the overall orientation of cellular and extracellular matrix components (e.
Acknowledgements The authors thank  Benoit Sorre  for granting access to the DSD2 confocal microscope,  Melina Durande  and  François Graner  for advice on orientation measurements,  Don Newgreen  for feedback on our manuscript. We thank the  Mouse Facility  staff of the  Institut Mondor de Recherche Biomédicale. We thank the  Imagoseine facility  for use of the Zeiss CSU-W1 spinning disk microscope. This research was funded by the  CNRS   Défi Mecanobiologie "MECHENSDEV" grant , by the  IdEx Université de Paris   ANR-18-IDEX-0001  grant, by the  Agence Nationale de la Recherche   ANR GASTROMOVE-ANR-19-CE30-0016-01  grant and by the  Institut National Pour la Santé de la Recherche Médicale (INSERM).
First, western immunoblots are limited to the availability of antibodies. Although highly specific and semi-quantitative, this method does not provide information about the relative abundance between selenoproteins. Alternatively, radioactive 75 Se (in the form of selenite) has been widely used to label selenoproteins in vivo and in vitro  [29] , and provides relative abundance data for abundant selenoproteins. Radiolabeled proteins are separated by different biochemical methods, including chromatography or SDS-PAGE, and are revealed by gamma-counting or autoradiography. 75 Se is a gammaemitter with a rather long half-life (120 days) and its use is restricted to a few laboratories. In addition, only abundant selenoproteins are visible by the autoradiography of SDS-PAGE. The presence of selenium in a molecule can be otherwise detected by elemental ion mass spectrometry, such as ICP MS, which is able to differentiate and quantify all natural isotopes of selenium.
Therefore, Figure  3a  illustrates the fold stimulation of UGA recoding efficiency in response to the concentration of added selenite. Interestingly, we noticed an important dose-response increase in the Sec insertion with both constructs, with a maximum of a 100-and 50-fold stimulation, respectively, for Luc UGA/SECIS GPX1 and Luc UGA/SECIS GPX4. The Sec insertion efficiency reached a plateau at 50 nM selenium for the Luc UGA/SECIS GPX4 construct, while higher selenium levels seemed to be necessary for the Luc UGA/SECIS GPX1 constructs. These data further confirm the importance of translational control in the selenium-dependent modulation of selenoproteins. To complement this analysis individually at the selenoprotein level, we performed western immunoblots with the cellular extracts from cells grown in the presence of various selenium levels ( 76 Se).
Materials The HEK293 cell line used in this study was purchased from Life technologies. For the HEK293 cells stably expressing Luc UGA/GPX1 or Luc UGA/GPX4, they were generated and validated in  [15, 22, 33]. Fetal calf serum (FCS), cell culture media and supplements were purchased from ThermoFisher Scientific (Waltham, MA, USA). Transferrin, insulin, 3,5,3 -triiodothyronine, hydrocortisone, EDTA, sodium selenite, and DTT were purchased from Merck (Darmstadt, Germany). 76 Se (99. 8%) and 77 Se (99. 2%) isotopically enriched selenites were provided by Isoflex (San Francisco, CA, USA). Antibodies were purchased from Abcam (Cambridge, UK) (GPX1, #ab108429; GPX4, #ab125066; SELENOF, #ab124840; SELENOM, #ab133681; SELENOO, #ab172957; SELENOP, #ab109514), ThermoFisher Scientific (TXNRD1, #LF-MA0015) and Merck (SELENOS, #HPA010025; TXNRD2, #HPA003323; SEPHS2, #WH0022928M2; Actin, #A1978).
1 s. UV 280nm and ICP MS signals were exported and treated with Microsoft Excel software to present chromatograms and isotopic graphs. Seven chromatographic fractions were defined with the elution times listed in Table  1. To integrate the total count of each selenium isotope ion per fraction, the background levels were determined and subtracted. The dose-response and kinetic curves were fitted using Kaleidragraph software with standard equations. For dose-response curves, it was as follows: y = a + ((b. x)/(k + x)), where k was the concentration of selenium allowing half saturation of the signal, and therefore referred to as k 1/2 throughout the manuscript. Kinetic curves were fitted with y = a + b. exp(-k. t), where the constant t 1/2 = (ln(2)/k), and the represented time allowing an increase or decrease in the signal by 50%.
Protein Gels and Western Immunoblotting Equal protein amounts (50 µg) were separated in Bis-Tris NuPAGE Novex Midi Gels and transferred onto nitrocellulose membranes using iBlot ® DRy blotting System (Ther-moFisher Scientific). Membranes were probed with primary antibodies (as indicated) and HRP-conjugated anti-rabbit or anti-mouse secondary antibodies (Merck). The chemiluminescence signal was detected using the ECL Select Western Blotting Detection Kit (GE Healthcare) and the PXi 4 CCD camera (Ozyme, Saint-Cyr-l'École, France). Image acquisition and data quantifications were performed with the Syngene softwares, GeneSys and Genetools (Ozyme), respectively.
METHODS Publicly available data from the Global School Health Survey (GSHS) were analyzed. Details on this self-administered survey can be found at http://www. who. int/chp/gshs and http://www. cdc. gov/gshs. Briefly, the GSHS was jointly developed by the WHO and the US Centers for Disease Control and Prevention (CDC), and other UN allies. The core aim of this survey was to assess and quantify risk and protective factors of major non-communicable diseases. The survey draws content from the CDC Youth Risk Behavior Survey (YRBS) for which test-retest reliability has been established  (22). The survey used a standardized twostage probability sampling design for the selection process within each participating country. For the first stage, schools were selected with probability proportional to size sampling.
The second stage involved the random selection of classrooms which included students aged 13-15 years within each selected school. All students in the selected classrooms were eligible to participate in the survey regardless of age. Data collection was performed during one regular class period. The questionnaire was translated into the local language in each country and consisted of multiple choice response options; students recorded their response on computer scannable sheets. All GSHS surveys were approved, in each country, by both a national government administration (most often the Ministry of Health or Education) and an institutional review board or ethics committee. Student privacy was protected through anonymous and voluntary participation, and informed consent was obtained as appropriate from the students, parents and/or school officials.
Data were weighted for non-response and probability selection. From all publicly available data, we selected all nationally representative datasets that included the variables used in the current analysis. If there were more than two datasets from the same country, we chose the most recent dataset. Thus, a total of 68 countries were included in the current study. The characteristics of each country or survey are provided in Table  1. For the included countries, the survey was conducted between 2009 and 2017, and consisted of 7 low-income (n=12,462), 27 lower middle-income (n=67,333), 20 upper middle-income (n=72,861), and 14 high-income (n=27,508) countries based on the World Bank classification at the time of the survey for the respective countries.
Indeed, certain types of fast food may be more detrimental to both physical and mental health than others. Future research should aim to investigate the present association using fast-food type as an outcome. It should also be noted that the validity of our question on fast food consumption has not been examined and thus is unknown. Fourth, there is much overlap between poverty and food insecurity. Thus, it is possible that the association observed in our study may also partly be explained by other aspects of poverty that could not be adjusted for in our analysis due to lack of data. Fifth, information on factors such as parent's SES, family structure, and other environmental factors (e. g. , availability of fast-food in the neighborhood) were not available in the dataset. Thus, the influence of these factors in the association between food insecurity and fast-food consumption could not be assessed.
Magnesium, a metabolite The role of magnesium in cell and tissue metabolism is complex and multifactorial. Accumulating evidence suggests that magnesium acts primarily as a key signaling element and metabolite in cell physiology. Consequently, the concept that magnesium is an electrolyte is too simplistic and obsolete  [18] [19] [20] [21] [22]. Magnesium is involved in all metabolic and biochemical pathways and is required in a wide range of vital functions, such as bone formation, neuromuscular activity, signaling pathways, bioenergetics, glucose, lipid and protein metabolism, DNA and RNA stability, and cell proliferation. The enzymatic databases list more than 600 enzymes with magnesium indicated as cofactor, and additional 200 are reported in which magnesium acts as an activator  [23]. However, it should be specified that, since it interacts directly with the substrate, magnesium is itself a substrate rather than a cofactor  [20, 24].
Acknowledgements The Authors wish to thank  Martin Kolisek ,  Massimo Galli ,  Stefano Rusconi ,  Stefania Caronni ,  Letizia Oreni ,  Lucia Merolle ,  Roberto Baricchi ,  Paolo Giorgi Rossi ,  Pamela Mancuso ,  Tommaso Fasano ,  Ornella Piazza ,  Taylor Wallace ,  Nana Gletsu-Miller ,  Martha Rodriguez-Moran , and  Moises Mercado-Atri  for their contribution in the clinical studies listed in Table 2.
Preparation of the target (SARS-CoV-2 Mpro) Protein Data Bank was used to download the crystal structure of SARS-CoV-2 Mpro with (PDB code: 6LU7 and resolution: 2. 16 Å). 37  The crystal structure was protonated and hydrogen atoms were added with their standard 3D geometry, automatic correction for any errors in the atom's connection and type was applied, and potential xation of the receptor and its atoms were done. Site Finder was applied for selection of the same active site of co-crystallized inhibitor using all default items and dummy atoms of the pocket were created. 38, 39 4. Docking of the tested b-adrenergic blockers to the viral Mpro binding site Docking of the previously prepared database composed of the tested twenty b-adrenergic blockers (1-20) and the cocrystallized inhibitor N3 (21) was performed.
The Particle-Mesh Ewald (PME) algorithm was used for treating the long-range electrostatic interactions, 53 whereas all covalent bonds including the hydrogen were constrained via the new LINear Constraint Solver (LINCS) algorithm. 54 At this point, each system became minimized and well-equilibrated at the proper temperature (303. 15 K), while being ready for the 100 ns duration molecular dynamics runs. Three MD simulation replica were carried out for each ligand-protein complex under NPT ensemble, Nose-Hoover thermostat and Parrinello-Rahman barostat for maintaining a canonical ensemble, and at an integration time step of 2 fs with no restrictions. The Verlet cut-off scheme (cut-off radius of 10 Å) was adopted for treating the long-range interactions. Data analysis was performed using the GROMACS tools via several build-in trajectory tools including the root-mean-square deviation (RMSD), radius of gyration (R g ), and RMS-Fluctuations (RMSF) to determine the molecular complex stability/validity in terms of MD performance, exibility, and conformation.
55 All MD data are reported with standard deviation of the multiple runs. For better estimation of the protein exibility, the difference RMSF (DRMSF) was estimated for each ligand-bound protein relative to the SARS-CoV-2 Mpro apo state (PDB code: 6Y84; atomic resolution 1. 39 Å), where DRMSF ¼ apo RMSFholo RMSF. The same previous preparation, minimization, equilibration, and 100 ns all-atom MD simulation production were applied to the Mpro apo state, except no ligand preparation was performed. The Hydrogen Bond Analysis and Distance Calculation Tool, within Visual Molecular Dynamics ver. 1. 9. 3 soware (VMD; University of Illinois, Urbana-Champaign, USA) were utilized. The cut-off values for hydrogen bond (donor-H/acceptor; DH-A) distance and angle were assigned at 3. 0 Å and 20 , respectively.
56, 57 These tools would allow respective estimation of the number/frequency of ligand-Mpro intermolecular hydrogen bonding as well as monitoring the distance changes between the specied ligand/ protein atoms (hydrogen bond donor and acceptors) over the whole simulation periods. Finally, the binding-free energy was estimated by the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) calculation using the g_mmpbsa module within GROMACS. The MM/PBSA calculations provided more insights regarding the magnitude of ligand-protein affinity, the nature of interaction, in addition to the residuewise contributions within the binding-free energy calculations. 58 Important MM/PBSA parameters for polar/solvation calculations were set at solvent dielectric constant (80 pdie), solute dielectric constant (2 pdie), radius of solvent probe (1.
The formed formazan crystals were dissolved using an acidied isopropanol (200 ml) and the absorbance of their solutions were recorded through a multi-well plate reader (l max ¼ 540 nm) against a reference wavelength (l max ¼ 620 nm). Finally, the cytotoxicity % of the tested compounds compared to the control cells (untreated cells) was calculated as follow: 3. 6. 2. Inhibitory concentration 50 (IC 50 ) determination. The Vero-E6 cells (2. 4 Â 104) were kept overnight at 37 C in 5% CO 2 inside 96-well tissue culture plates. 1Â PBS solution was used to wash the cell monolayers for only one time which were then treated with different serial dilutions of the examined compounds together with a xed dilution from the virus (hCoV-19/Egypt/NRC-03/2020 (Accession Number on GSAID: EPI_-ISL_430820)) following TCID 50 test and kept at RT for 1 h before starting incubation.
Molecular docking In order to study the binding characteristics of the b-adrenergic blockers with the binding site of Mpro, molecular docking studies were performed by Molecular Operating Environment (MOE, 2019. 10) soware. The SARS-CoV-2 Mpro has a Cys-His catalytic dyad, and the substrate-binding site is located in a cle between domains I and II. The N3 inhibitor is tted inside the substrate-binding pocket of SARS-CoV-2 Mpro showing asymmetric unit containing only one polypeptide. The X-ray crystallographic structure of Mpro (PDB ID: 6LU7)  37  was downloaded from the Protein Data Bank (PDB). 38 The downloaded protein is   Many poses were obtained with better binding modes and interactions inside the receptor pocket. The poses with the best scores (related to the stability of the pose) and RMSD values (related to the closeness of the selected pose to the original ligand position inside the receptor pocket) were selected.
It worth noting that the profound free-binding energy of the top-stable b-blockers (CAR and NEB) correlates well with the obtained initial docking score ranking (Table  1 ). For gaining more insights regarding ligand-residues interactions, the binding-free energy decomposition within the g_mmpbsa module was utilized to identify the key residues involved within the obtained binding free energies. 58 Interestingly, similar residue-wise energy contribution patterns were assigned for both the b-blocker agents (Fig. 9A ). This was of no surprise since both ligands depicted comparable energy terms as well as total binding-free energy values. Residues of the S2 subsites and their vicinal residues showed signicantly high residue-binding energy contributions with values up to twodigit kJ mol À1. Residues such as; Glu-47, Asp-48, Glu-55, Asp-56, Asp-187, and Asp-197 illustrated very high energy contributions ranging from À14.
Acknowledgements The authors gratefully acknowledge the  Deanship of Scientic Research at Umm Al-Qura University  for supporting this work by Grant Code " 20-SCI-4-13-0001 ". Also, the simulations in this work were performed at  King Abdulaziz University's High Performance Computing Center (Aziz Supercomputer)  (http:// hpc. kau. edu. sa)", the authors, therefore, acknowledge with thanks the center for technical support.
73 AE 0. 15 0. 77 AE 0. 27 S2 Met-49 À0. 12 AE 0. 40 0. 93 AE 1. 55 0. 46 AE 0. 43 1. 53 AE 0. 43 Tyr-54 0. 39 AE 0. 25 0. 02 AE 0. 10 0. 33 AE 0. 26 0. 48 AE 0. 26 His-164 0. 21 AE 0. 17 0. 31 AE 0. 03 0. 32 AE 0. 18 0. 29 AE 0. 16 Asp-187 0. 61 AE 0. 15 0. 84 AE 0. 22 0. 87 AE 0. 05 0. 96 AE 0. 06 Arg-188 0. 36 AE 0. 09 0. 66 AE 0. 38 0. 68 AE 0. 08 0. 85 AE 0. 35 S3 Met-165 0. 15 AE 0. 23 0. 27 AE 0. 03 0. 34 AE 0. 16 0. 35 AE 0. 29 Leu-167 0. 49 AE 0. 33 0. 61 AE 0. 24 0. 63 AE 0. 17 0. 72 AE 0. 15 Gln-189 À0. 11 AE 0. 19 0. 19 AE 0. 56 0. 36 AE 0. 39 0. 40 AE 0. 43 Thr-190 À0. 52 AE 0. 26 À0. 23 AE 0. 48 0. 08 AE 0. 56 À0. 04 AE 0. 19 Gln-192 À0. 82 AE 0. 43 À0. 53 AE 0. 29 À0. 19 AE 0. 68 À0. 43 AE 0. 22 a Relative difference root-mean-square uctuation (DRMSF) AE standard deviation was estimated for each ligand-associated Mpro protein relative to the SARS-CoV-2 Mpro apo-state (PDB code: 6Y84; atomic resolution 1.
Study Design and Participants The current study was part of a randomized controlled trial registered on clinicaltrials. org (NCT03215784), which was designed to evaluate the effects of fish oil and probiotics supplementation throughout pregnancy on women with pre-gestational obesity. The study was conducted at the Maternidade Escola, between January 2015 and July 2017. This is a referral hospital, belonging to the Federal University of Rio de Janeiro, dedicated to providing pre-natal and delivery care and puerperal consultations to the local community. Women were recruited up to 13 weeks of pregnancy and the inclusion criteria were age between 19-35 y, pre-gestational body mass index (BMI) between 18. 5 and 24. 9 kg/m 2 (lean) or ≥30 and ≤40 kg/m 2 (obesity class 1 and class 2;  [29] ), absence of pre-existing infectious or chronic disease, except for obesity, a single fetus, and non-smoker.
The Dual Agilent Jet Stream Electrospray Ionization Source was operated separately in positive and negative ion modes. The detailed description of the LC-MS analyses can be found in Supplementary File S1. Chromatogram Pre-Processing and Lipid Annotation Data pre-processing, including mass and CCS re-calibration and feature finding, was carried out using the packages IM-MS Reprocessor, IM-MS Browser and Mass Profiler from the MassHunter suite vB. 08. 00 (Agilent Technologies, Santa Clara, CA, USA). The resulting data matrices were processed using a KNIME pipeline comprising both KNIME native nodes and integrated R scripts. Feature annotation was performed based on the AccurateMassSearch node of the OpenMS library  [42].
Isoprostanoids in Placental Tissue Isoprostanoids in placenta were determined based on a microLC-MS/MS method  [43]  after lipid extraction by the Folch method  [41] , which were then mixed with a mixture of internal standards, followed by alkaline hydrolysis. MS analysis was performed in an AB Sciex QTRAP 5500 (AB Sciex, Framingham, MA, USA) with an electrospray ionization source operated in negative mode. Quantification of isoprostanoids was performed with the MultiQuant 3. 0 software using specific internal standards. The detailed description of isoprostanoids analysis by microLC-MS/MS is described in Supplementary File S1.
In the final model, only significant variables that improved the adjustment of the model were kept (p-to-remove ≥ 0. 05; p-to-persist < 0. 05). The multiple regression models were further assessed by analysis of residual plots that were checked to determine if they were randomly distributed. Data analyses were performed with GraphPad Prism v7. 0 (GraphPad Software, San Diego, CA, USA) and Statgraphics Centurion v18 (Statgraphics Technologies, Inc. ; The Plains, VA, USA). In all analyses, p < 0. 05 was considered for rejection of the null hypothesis.
Mean values and statistical errors calculated from these data are K2 =1067 ± 192, k-1 = 325 ± 84 M -1 s -1 , and k1 = 0. 36 ± 0. 14 M -1 s -1. Table  1. Equilibrium and rate constants deduced from spectrophotometric experiments with mixtures of DMDSe, GSH, and GSSG. To determine more precisely the value of the rate constants, stopped-flow spectrophotometry was employed. To obtain k -1 , MeSeH was produced by reduction of DMDSe with a substoichiometric amount of TCEP and was immediately mixed with various amounts of GSSG in the stopped-flow instrument. To obtain k -2 , DMDSe was mixed with GSH. The changes in absorbance at 252 nm were recorded. The kinetic traces from four (for k -2 ) or six different mixtures (for k -1 ) were fitted to the theoretical model. The resulting constants are given in Table  2.
Dose-response was measured at 0, 2, 4, 6, 8 and 24 h in RD MOPS glycerol and 0, 1, 2, 3, 4, 5 h in RD MOPS glucose. Time points for analysis were selected at maximum GFP emission, since the levels reached a maximum and then decreased presumably as arabinose was depleted and metabolism switched to glycerol. Measurements in plant extracts were taken at daily intervals over 5 days. Four sample reps were measured per test, and the experiment repeated three times. GFP is expressed as relative fluorescent units after subtraction from the vectoronly control (pKC026 or pWSK29) data and normalized for cell density to OD 600 of 1. 0. Arabinose concentrations were transformed (Log 10 ) for graph plotting and linear-regression analysis. Gene-expression analysis was measured directed by quantitative reverse transcriptase PCR (qRT-PCR) as previously  [16]  from STEC (Sakai) grown in minimal M9 medium (1 × M9 salts, 1 mm MgSO 4 , 0.