dataesr/scientific-paragraphs-categorization huggingface dataset:
Motif's analysis For every set of SNPs (rhythmicity CVe and mean, contractility CVe, and mean), the coordinates (dm3 genome assembly) were extended 75 nt to both sides and their corresponding fasta sequences were retrieved. TF binding motifs were identified using RSAT peak motifs  (Thomas-Chollier et al. , 2012)  (default parameter) using two distinct background models: intrinsic (frequencies of short k-mers in the input sequences to calculate the enrichment of longer k-mers) and random (set of random genomic regions matching length and nucleotide composition using biasaway; Worsley  Hunt et al. , 2014). To reduce the redundancy of the found motifs (PSSMs), we applied the RSAT matrix-clustering  (Castro-Mondragon et al. , 2017)  algorithm (default parameters) to find clusters of motifs. The clustered motifs were manually annotated by comparison with PSSM databases (Cis-  BP Weirauch et al.
RNA-seq and data analysis Day 25 hiPSC-CMs were transfected with 25nM final concentrations of siRNA against PAX9 and with scrambled control siRNAs. After 3days, cells were pelleted and resuspended in 500µL TRIzol reagent followed by total RNA extraction. Library preparation and sequencing of the samples was done at La Jolla Institute of Immunology (La Jolla, CA). FASTQ files were processed using nf-core/rnaseq  (Ewels et al. , 2021)  with Nextflow Version: 21. 03. 0. edge. Differential gene expression was determined using R/DESeq2  (Love et al. , 2014)  and GO term enrichment was done using gprofiler2  (Kolberg and Raudvere, 2021). All analysis scripts can be downloaded at https://github. com/gvogler/eLife-2022-Saha-et-al (copy archived at swh:1:rev:b102c48b85976216c37d2e5aed060670535192f6; Vogler, 2022b).
Immunostaining of hiPSC-CMs Three days post-transfection cells were fixed with 4% paraformaldehyde for 30 min and blocked in blocking buffer (10% Horse Serum, 10% Gelatin, and 0. 5% Triton X-100) for 20 min. hPSC-CMs were stained with sarcomeric protein TNNT2 (Catalog #: HPA017888 Lot #: A91786, Sigma, dilution 1/200), secondary antibody Goat anti-Rabbit IgG Alexa Fluor 568 (Invitrogen, 1/500) and DAPI (1/1000) in Blocking Buffer. Cells were imaged with ImageXpress Micro XLS microscope (Molecular Devices) and custom algorithms were used to quantify the area of TNNT2 mask per cardiac nuclei. All experiments were performed at least in biological quadruplicates.
Acknowledgements The project leading to this publication has received funding from  Fondation de France  ( N°00071034 ) and from  Excellence Initiative of Aix-Marseille University  -A* MIDEX  (A *Midex International ) to LP, from the  National Institute of Health  ( R01 HL148827-03 ,  R01 HL149992-02 ,  R01 AG071464-01 ) and from the  Department of Defence  ( W81XWH-21-1-0104-01 ) to AC and from  National Institute of Health  ( R01 HL132241  and  R21 AG061598 ) to KO. We thank the  Bloomington Drosophila Stock Center (BDSC)  and the  Vienna Drosophila Research Center (VDRC)  for fly stocks. Centre de Calcul Intensif d'Aix-Marseille  is acknowledged for granting access to its high-performance computing resources.
3b ), thus obtaining a model where STP results from vesicle depletion only. Each GC received exactly two driver and two supporter MF inputs with random and pairwise distinct identities (Fig. 3a ). In this reduced model, an instantaneous and persistent switch of MF firing rates generates an average postsynaptic current (I syn (t)) for each vesicle pool that is remarkably simple. It features a sharp transient change, followed by a mono-exponential decay to a steady-state synaptic current amplitude, A s , (Fig. 3c ) and can be generally expressed as I syn ðtÞ = A s + A t e À t τ syn ð1Þ Here, A s is a time-invariant component and A t e À t τ syn is a transient component with synaptic relaxation time constant τ syn (Fig. 3c ) and amplitude A t. This transient component determines the synapse's ability to encode the passage of time.
We refer to these pools as'slow' and'fast', respectively. In the Hallermann model  38  , the slow pool is refilled by vesicles from the fast pool. For the sake of mathematical tractability, we modeled the pools as being refilled independently (see scheme in Fig. 1 ). To model vesicle depletion, we use the variables x slow and x fast , denoting the fraction of neurotransmitter available at the slow and fast vesicle pool. The state of the pools between GC i and MF j at time t is then described by where, τ slow ref and τ f ast ref are the time constants of recovery from vesicle depletion for the slow and fast pools, and are identical for all synapses. The variables u slow ij ðtÞ and u f ast ij ðtÞ denote the pools' respective release probabilities at time t. Experimental data show that, in response to trains of action potentials, MF-GC synapses approach synaptic steadystate transmission with a long time constant  38, 39.
Reduced CC model The reduced synaptic model included only two synapse types. We also neglected facilitation and desensitization, yielding constant release probabilities and constant normalized quantal size: We obtain for the vesicle pool dynamics: u slow ij ðtÞ _ x slow ij ðtÞ and the total synaptic weight becomes W ij ðtÞ = N α slow Á p α v,slow Á x slow ij ðtÞ + N α f ast Á p α v,f ast Á x f ast ij ðtÞ: ð25Þ Here the index α denotes membership in the driver or supporter category. The synaptic currents of the reduced model are computed as in the full model. Each GC receives exactly two driver and two supporter MF inputs with random and pairwise distinct identities. To eliminate any non-synaptic dynamics from the reduced model, we removed the GC membrane time constant yielding GC dynamics that follow the synaptic input instantaneously: gc i ðtÞ = α i Á maxðI gc,i ðtÞ À θ i ,0Þ: ð26Þ Finally, GC threshold and gain adjustments were carried out similarly to the full CCM STP where instead of Eqs.
Acknowledgements A. B. thanks  Gianluigi Mongillo  and  Zuzanna Piwkowska Zvonkine  for helpful discussions. We thank the  DiGregorio Lab  for feedback on this manuscript. This work is supported by the  Institut Pasteur ,  Centre National de la Recherche Scientifique ,  Fondation pour la Recherche Médicale  ( FRM EQU202003010555 ),  Fondation pour l'Audition  ( FPA-RD-2018-8 ),  BioPsy Laboratory of Excellence , and the  Agence Nationale de la Recherche  ( ANR-17-CE16-0019 , and  ANR-18-CE16-0018 ,  ANR-19-CE16 0019-02 ,  ANR-21-CE16-0036-01 ), which were awarded to the laboratory of DAD.
Docking experimentation finds the best complexes of ligand/receptor according to the energy of binding. The crosslinking of these theoretical data with experimental data can further define the agonist effect of a small molecule toward a receptor. Inverse docking  [44]  allows for screening of a ligand on multiple receptors. In this way, it is possible to predict the preferred target of a ligand. In this study, we present the purification of triterpenes of Alnus glutinosa bark extract and their activity on T. gondii through the determination of their IC 50 values. An inverse docking process was then used to hypothesize the potential target of the most active compounds with the AMIDE reverse docking framework  [8, 43]. The set of proteins was constructed using the Protein Data Bank (PDB) structures available on the Research Collaboratory for Structural Bioinformatics (RCSB) site (mid-2019) after elimination of redundant and low-quality models.
NMR analysis Compounds to analyze were dissolved in 600 lL CDCl 3. 1 H, 13 C, and 2D-NMR (HSQC, HMBC, and COSY) experiments were performed at 298 K on an Avance AVIII-600 (Bruker, Germany) spectrometer ( 1 H at 600 MHz and 13 C at 150 MHz) equipped with a 5 mm TCI cryoprobe. 2D-NMR experiments were performed using standard Bruker microprograms (TopSpin 3. 2 software).
Semi-preparative HPLC purification Terpene-rich fractions were solubilized in MtBE and then purified using a semi-preparative Uptisphere Strategy C18-HQ 250 Â 10. 0 mm (5 lm) column (Interchim, Montluçon, France) and a Dionex HPLC chain equipped with an ASI-100 autosampler, a P580 pump, a UVD 340S diode array detector, an automated fraction collector Ultimate 3000, and Chromeleon 6. 8 software. Gradient elution with a constant flow rate of 5 mLÁmin À1 was used, starting with 90% of eluent A (CH 3 CN) and 10% of eluent B (H 2 O with 0. 025% trifluoroacetic acid) reaching 100% of eluent A in 5 min, followed by isocratic elution with 100% of eluent A during 25 min. The effluent was monitored at 200 nm. Six triterpenes were purified: 4. 7 mg of betulinic acid (3) (tr = 7. 0 min), 31 mg of lupenone (6) (tr = 7.
Statistical analysis of in vitro screening Statistical analyses were performed using R software (version 4. 1. 1) with RStudio IDE (version 1. 4. 1717). Before pairwise comparison, data normality was assessed by a Shapiro test. Variance homogeneity between samples was then checked using a Fisher test before the pairwise comparisons of screening results with a classical Student t-test when both the normality and variance homogeneity null hypothesis were verified, or a Welsh two-sample t-test when only the null hypothesis about data normality was verified. Finally, a Wilcoxon rank-sum exact test was used for all other cases. Significance levels for p-values are: lower than 0. 05 (*), lower than 0. 01 (**), lower than 0. 001 (***).
gondii were obtained from the Protein Data Bank (https://rcsb. org/) in mid-2019. Protein structures obtained by X-ray crystallography and corresponding to a resolution lower than 3 Å were selected. Thus, a final set of 87 non-redundant structures, including structures with co-crystallized co-factors was created. Then, each protein was submitted to AMIDE to be cut into 12 overlapping docking boxes. The number of AutoDock runs per box was set to 20.
Purification of the compounds of interest The n-heptane bark extract of Alnus glutinosa was first fractionated by centrifugal partition chromatography and then, the triterpene-rich fractions were purified by semi-preparative reversed-phase HPLC. Seven compounds were purified and their structures were identified by NMR data analysis as 6 known lupane-type triterpenes: betulin (1)  [25, 39] , betulinaldehyde (2)  [2, 25] , betulinic acid (3)  [25, 39] , betulone (4)  [26] , lupeol  (5)   [25, 39] , and lupenone (6)  [4]  together with the b-sitostenone (9), a phytosterol  [32]  (Fig. 1 ). The activity of the purified compounds (1-5, 9) was then evaluated against Toxoplasma gondii except for lupenone (6) which was not soluble in DMSO.
Target identification of most active compounds by inverse docking An inverse docking strategy using a dataset of T. gondii proteins obtained from the Protein Data Bank was performed to identify the potential biological targets of betulone, betulonic acid, betulonic acid methyl ester, and betulinic acid on T. gondii, and thus to obtain an initial suggestion of the mechanism of action. Betulone was first screened alone on the 87 proteins to identify the most promising proteins for the docking of the three other compounds. Table  3  presents data related to the 10 most relevant clusters identified from the docking of betulone; ranking is operated according to scoring 2 (Eq. (  2 )) which considers and compares cluster population, the free energy of binding, as well as scoring 1 (Eq. (  1 )). The values of scoring 2 spans between 0.
6577 and 0. 7887 and thus can be considered close. To narrow down the number of proteins used as targets in the re-docking experiments, a visual study of the 10 complexes was performed with PyMol 2 software. Betulone was selected as a reference since it showed the best in vitro activity. The best complexes were expected to present the ligand in a cavity and show electrostatic binding with the protein  [38]. Proteins with PDB ID 2O50, 3HZT and 3BYV matched the requirements and were therefore selected. The protein with PDB ID 4JRN is equivalent to the PDB ID 3BYV in the defined region; in addition, the poses of the betulone best cluster were similar in the two structures. As a result, the protein with PDB ID 4JRN was not retained in the selection. Based on previous results, the four most relevant compounds previously identified from in vitro experiments (betulone, betulonic acid, betulonic acid methyl ester, and betulinic acid) were submitted to docking simulations focused on the binding site previously identified for the three proteins.
This refined docking was performed with 150 AutoDock runs to better qualify the poses. Results are presented in Table  4. The high population and low free energy of binding of the different complexes suggest strong binding between the four ligands and the three proteins. The additional AutoDock value of ligand efficiency was added to help in the selection of the best complex. All the compounds and proteins showed high affinity. The first hit was the complex betulonic acid methyl ester-3HZT with a ligand efficiency of À0. 39, followed by the complex betulone-3HZT with a ligand efficiency of À0. 36. The two cited complexes are described below as well as the first hit complex betulone-2O50 identified in Table  3. The best ligand efficiencies were obtained for the complexes with the ligands betulone and betulonic acid methyl ester (BAME) with the proteins with PDB IDs 3HZT, 2O50, and 3BYV.
They were visualized with PyMol (Fig. 3 ). Each of the ligands was in a folded area that could be assimilated to a pocket (see inserts of Fig. 3 ). In addition, betulone and BAME were systematically in the binding site of original ligands for PDB IDs 3HZT and 2O50 which ensures that the ligands were docked to an area of interest. This comparison was not possible   with PDB ID 3BYV since it does not have a co-crystallized ligand. PyMol visualization of the BAME and PDB ID 3HZT protein complex shows that BAME was located in a pocket in which the co-crystallized ligand (PDB ID J60) is also located in the original 3HZT structure (Fig. 3d ). Within 3 Å, the interaction analysis tool indicated polar contact between the ketone in position 3 and the residue GLU-160. Within 3. 5 Å, the methyl ester in position 28 and the isopropyl group in position 19 of BAME seem to strongly interact with the residues of the cavity together with the CH 2 and CH 3 groups of the lupane backbone.
This suggests strong interactions between BAME and the protein. These observations reinforce the conclusions drawn from the results shown in Table  4 : there is a high affinity between BAME and the PDB ID 3HZT protein. The complex betulone-3HZT showed the second-best free energy of binding equal to À11. 52 kcal/mol and corresponded to a cluster population of 149. Visualization of the complex with PyMol showed that betulone was placed in the same cavity as BAME but was inverted lengthwise (Fig. 3a ). Despite this, betulone still showed strong interactions with this receptor (Fig. 4b ). Within 3 Å, polar contacts could be determined between the ketone in position C3 and GLN-367, and a polar area was defined between the C18 hydroxyl and residues GLU-154/VAL-155/TYR-156. Non-polar contact was established between the methyl group in position 27 with the residue VAL-90.
Finally, many additional contacts were identified within 3. 5 Å. Since the ligand can move within the cavity, these contacts provide a strong interaction between betulone and the protein. Finally, the interaction between betulone and PDB ID 2O50 was studied since it was the first result obtained with the reverse docking process (Table  3 ). Within 3 Å, PyMol visualization of the complex betulone-2O50 (Fig. 4c ) highlighted electrostatic interactions between residue ARG-184 and ketone in C3 of betulone and between residues PRO-187/GLY-188 and CH 2 -OH in position 28 of betulone. Within 3. 5 Å, the lupane backbone seems to interact with the protein and a pi-stacking is even supposed with a tyrosine. Together with the backbone, the ketone and the CH 2 -OH functions seem to stabilize betulone in the cavity.
8% with betulin and betulinic acid, respectively reached 97. 6% with betulonic acid  [6]. Isah et al. also showed structural modification especially at C3 and C27 of the parent backbone of pentacyclic triterpenes improved antiparasitic activity in some cases and loss of activity in others  [19]. All these results confirm that ketone in C3 and hydroxyl or aldehyde on C28 improves the ability of lupane type triterpenes to inhibit parasite growth. To investigate the mechanism of action of betulone on T. gondii, an inverse docking study of betulone on 87 proteins was carried out, highlighting a strong interaction between betulone and three T. gondii proteins identified as CDPK3 (PDB ID 3HZT), ENR (PDB ID 2O50), and a rhoptry domain kinase ROP8 (PDB ID 3BYV). These proteins are not found in the human body, which is interesting in terms of selectivity towards parasitic rather than human biological targets.
CDPK3 is a calcium-dependent protein kinase involved in the specific calcium-signaling pathway in plants and apicomplexans. The inhibition of this protein could lead to the alteration of motility, cell invasion, and egress disruption  [45]. There are only few publications describing the activity of pentacyclic triterpenes on CDPK. Nevertheless, one of them mentions that ursolic acid derivatives are active against T. gondii in vivo by inhibiting the proliferation of the parasite in the mouse abdominal cavity and in the same publication, Luan et al. identified CDPK1 (PDB ID 6BFA) as a potential target  [23]. This protein is also involved in T. gondii egress regulation as well as CDPK3  [22]. These two results support the hypothesis that CDPK3 is a potential target of lupane type triterpenes.
gondii tachyzoite growth, but are also effective against other apicomplexan parasites, such as Plasmodium. ENR is inhibited by the antibacterial compound triclosan which inhibits in vitro growth of T. gondii with IC 50 of 3. 0 lM  [41]. Triclosan was found to inhibit P. falciparum growth with an IC 50 of 1 lM  [28]. The co-location of betulone and triclosan in the ENR binding site is consistent with the likely high affinity between betulone and ENR. PDB ID 3BYV refers to rhoptry antigen kinase domain ROP8. The rhoptries can be released in the host cell in the internalization process. Even if the mechanism is not well known, these proteins could participate in a momentary breach in the cytoplasmic membrane of the host cell. According to Qiu et al. , this X-ray crystal structure was catalytically inactive but remains a template for the study of the major kinase ROP8  [33].
Acknowledgements. The forestry harvesting company  Hubert Cie  ( Illy, France ) and the " Office National des Forêts" section of Signy L'Abbaye (Signy L'Abbaye, France ) are acknowledged for tree bark collection. The authors thank the  HPC-Regional Center ROMEO  (https://romeo. univ-reims. fr/)and the  Chaire MAgICS (Université de Reims Champagne-Ardenne)  for their support and the  Centre Hospitalier Universitaire de Reims and Grand Est region (France). This research was partially funded by the  Interreg North- West Europe program :  AgriWasteValue , grant number  NWE 750.
These observations, together with model data, will examine the role of reactive nitrogen in the pollution of Mexico City. A revaluation of NH 3 emission sources contribution in the Mexico City inventory is suggested. Measures to mitigate NH 3 emissions and reduce these positive trends are important, given that NH 3 is closely linked to secondary aerosol formation and the deterioration of ecosystems. Data availability. The UNAM and Altzomoni FTIR data used in this study are available at https://doi. org/10. 5281/zenodo. 7199948  (Herrera et al. , 2022)  Financial support. This research has been supported by the Consejo Nacional de Ciencia y Tecnología (CONACYT) (PhD grant no. 665043). Review statement. This paper was edited by Jayanarayanan Kuttippurath and reviewed by two anonymous referees.
The meteorological data for both FTIR stations are available at https://www. ruoa. unam. mx/csv_data/unam/ minuto. php (RUOA, 2022a) and https://www. ruoa. unam. mx/csv_ data/altz/minuto. php(RUOA, 2022b). The IASI ANNI-NH 3 -v3 L2 data are freely available and can be accessed through the AERIS database at https://iasi. aeris-data. fr/NH3/ (AERIS, 2022). MODIS active fire snapshots are available at https://wvs. earthdata. nasa. gov/ (NASA, 2022). The code of the HYSPLIT model can be obtained from https://www. arl. noaa. gov/hysplit/getrun-hysplit/ (NOAA, 2022). Last access to all URLs: 14 October 2022. Author contributions. BH is the main author of the paper, analysed the data, made most of the figures, and wrote the text. WS contributed to the data analysis. MG contributed to create some figures.
Finally, ALPINE provides the largest sample of C + -detections, making it possible for the first time to study the demographics of C + -line emitters at z∼4-6 [64]. More importantly, the calculated C + -line luminosity functions (LF) allow us to compare with the volume density of C + -emitters at z∼0 [65]. The C + -LF results provide the quantitative constraints on the redshift evolution of C + -emitter as well as the estimates of molecular gas density at z∼4-6. We note the unique aspect of ALPINE data, which is that the serendipitous C +detections provide the data to compute the incompleteness corrections to the C + -LF  [43]. In Section 3, we summarize the results of ALPINE on the topics above in more detail.
[65] ) as well as other literature measurements at higher redshifts. It is important to note that the C + LF at z∼5 derived from the ALPINE main sample is a lower limit, as it does not include serendipitously detected C + emitters, which can add to the number counts (see  [43] ). The incompleteness correction of the LF is very uncertain as it depends on the serendipitous sample, which is too small and covers only a narrow luminosity interval (mostly high-luminosity end at L C + > 10 8. 5 L ). In addition, the serendipitous C + emitters seem to be clustered with the main targets (see below); therefore, they are not representative of the field population, which adds additional uncertainty. Furthermore, the ALPINE data are not able to constrain the C + LF at L C + < 10 8 L due to the survey limits.
Is C + a Good Tracer of Star Formation at z > 4? A correlation between C + emission and total SFR is expected from studies crossing cosmic time from z = 0 to z∼2  [45, 48, 85, 86]. Such a relation is fundamental as C + is one of the strongest far-IR lines and is now widely observed in high-z galaxies. The far-IR continuum (a more-common SFR indicator;  [87] ) is significantly fainter; hence, it is much more time-consuming to detect. While probed by large datasets at local redshifts, the C + -SFR relation at higher redshifts is uncertain. Specifically, several studies suggest a population of C + underluminous galaxies due to their observed low C + /L FIR ratios compared to lower redshifts  [31, 32, 46, [54] [55] [56] 88 ]. The origin of C + from various regions in the galaxy (HII regions, diffuse neutral and ionized ISM and PDRs; [89,90]) add additional complications.
[26] ) and by applying the Kennicutt  [87]  relation, SFR UV [M yr -1 ] = 1. 59 × 10 -10 L 1500 /L converted to a Chabrier  [41]  IMF. For the calculation of the infrared SFR (SFR FIR ), the conversion from ALMA-measured single-point photometry at rest-frame ∼150 µm to total far-IR luminosity (L IR ) is crucial. The conversion depends strongly on the assumed shape of the far-IR SED (itself depending on many parameters including the warm and cold dust temperature), which is unknown as only one far-IR data point exists for the ALPINE galaxies. To compute the conversion factor, an average empirical far-IR template is created using the same approach as outlined in Section 3. 1. 2 (for details, see  [25] ). The far-IR SFRs for far-IR-continuum-detected galaxies are derived via SFR FIR [M yr -1 ] = 1.