dataesr/scientific-paragraphs-categorization huggingface dataset:
11%. Principal component analysis shows the overall consistency of the replicates and the relative positions of the various experimental groups relative to the monolayer controls (Supplementary Figs. S3C, D ). We identified 5397 (Dataset S01) and 1439 (Dataset S02) DEGs from enzymatic dissociation and TGF-β/TNF-αinduced RPE-EMT, respectively. Unsupervised hierarchical clustering of these DEGs in Figs. 5A  and B  showed distinct upregulated and downregulated patterns among different conditions in enzymatic dissociation and TGF-β/TNF-αinduced EMT. We found that 780 DEGs were common to both the dissociation and TGF-β/TNF-α EMT-induced samples (Supplementary Fig. S1A ; Dataset 03). We plotted top 20 transcription factors that were upregulated (red) and downregulated (blue) during dissociation (Supplementary Fig.
Dysregulated Axon Guidance Signaling Is Implicated in RPE-EMT We next performed a gene ontology-based enrichment analyis of the differentially expressed RPE-EMT associated genes (Datasets S04, S05, and S06). One of the most significant, and potentially interesting, EMT-enriched canonical pathways was the axon guidance signaling pathway (dissociation: -log[P value] = 6. 4; TGF-β/TNF-α: log[P value] = 14. 8) (Fig. 5E ). The second most enriched pathway arising from the IPA analysis was hepatic fibrosis/hepatic stellate cell activation, with enriched genes COL8A2, MMP2, LAMA1, IGFBP5, PDGF, MYH8, TGFBR2, and SERPINE1. Although, as the name implies, this pathway includes molecules related to response to liver damage, the molecules identified in these pathways also potentially modulate the tissue fibrosis that develops in the retina as part of the pathology associated with neovascular AMD.
Upstream Regulators of RPE-EMT To investigate the cascade of transcriptional regulators that are upstream and presumably regulate the observed RPE-EMT DEGs, we analyzed the transcriptomic data using the "upstream regulator analysis" (URA) module in IPA. The URA algorithm uses overlap of p-value and an activation z-score to identify regulators that have been shown experimentally to ellicit gene expression patterns observed in a dataset. 24 We filtered for regulators that were predicted to be activated or inhibited at least 1 time point from the dissociation EMT-induced samples (Dataset S07) and at least 1 dose from the TGF-β/TNF-α-induced EMT samples (Dataset S08) and from genes that overlapped from both EMT types (Supplementary Fig. S1D-J ; Dataset S09). We clustered the resulting URs based on absolute value of the IPA activation score at any dissociation time/TGF-β/TNF-α dose to identify temporal and dose dependant regulated patterns.
65 or instance, URA of the RPE EMT datasets implicated several miRNAs already implicated in tumor invasion and metastasis, including miR-210 (ovarian cancer  66  ), miR-122 (hepatocellular carcinoma  67  ), miR146 (non-small cell lung cancer  68  ), miR-124-3p (bladder cancer  69  ), and miR-30c-5p (gastric cancer  70  ). Together, our data establish a transcriptional hierarchy during enzymatic dissociation and TGFβ/TNF-α-induced RPE-EMT and identify number of master switches and nodes in its temporal and dose-dependent regulation. In conclusion, we have delineated many of the regulatory interactions underlying both the enzymatic dissociation and TGF-β/TNF-α EMT models. The results identify several key RPE-EMT regulatory hubs, including axon guidance signaling and other key kinases, transcription factors, and miRNAs.
We hope the extensive human RPE transcriptional datasets that we have generated, which are publicly available and can be explored interactively, will provide a useful resource to the vision research community for exploring the transcriptional changes and biological pathways involved in RPE-EMT. This information will hopefully aid in the development of therapeutics that focus on modulating the role of RPE-EMT in retinal disease. Additionally, developing the ability to inhibit EMT will have implications in the development of RPE transplantation-based therapies, because one of the challenges of safe and effective RPE transplantation is achieving cell purification and transfer without inducing EMT in either the donor or the host cells. 10, 71, 72.
Acknowledgments The authors thank  Brendan Lilley ,  James T. Handa ,  Derek Welsbie ,  Noriko Esumi ,  Xitiz Chamling ,  Bibhudatta Mishra ,  Ravi Chakra Turaga ,  John Fuller ,  Pingwu Zhang ,  Claire Bell ,  Xue Yang , and  Xiaomei Han  for their insightful suggestions and discussions. Supported by the  NIH  ( P30EY001765 ),  Thome Foundation ,  Beckman Foundation ,  Maryland Stem Cell Research Fund, Knights Templar Eye Foundation ,  Foundation Fighting Blindness, Research to Prevent Blindness, Bright Focus Foundation , and  Guerrieri Family Foundation. Disclosure:  S. R. Sripathi , None;  M. -W. Hu , None;  M. M. Liu , None;  J. Wan , None;  J. Cheng , None;  Y. Duan , None;  J. L. Mertz , None;  K. J. Wahlin , None;  J. Maruotti , None;  C. A. Berlinicke , None;  J. Qian , None;  D. J.
SCFA production in feces was quantified by 1D 1H NMR in water-extracted fecal samples (560 µL) with TSPD4 as an internal standard. Fecal water was obtained as previously described  [37]. Measurements on muscle fibres' cross-sectional area (CSA) were performed on muscle 10 µm thick cross-sections obtained at -20 • C using a cryostat (HM500M Microm International, Fisher Scientific, Illkirch, France) and labeled with anti-laminin-α1 (L9393-Sigma). Observations and image acquisitions were captured with a high-resolution, cooled digital DP-72 camera coupled to a BX-51 microscope (Olympus) and Cell-D software (Olympus Soft Imaging Solutions, Münster, Germany). The images analysis was performed using the Visilog 6. 9 software (Noesis, Crolles, France).
The aqueous phase of the subcutaneous adipose tissue was washed again with 400 µL of chloroform before being centrifuged and transferred to an RNAse-free column (invitrogene). RNA samples purity was verified using NanoDrop spectrophotometer (ND-1000, NanoDrop Technologies Inc. , Wilmington, DE, USA) and RNA (1 µg) integrity was verified on 1% agarose ethidium-bromide-stained gel. cDNA was obtained from 1 µg of RNA using the high-capacity cDNA reverse transcription kit (AB Applied Biosystems, Foster City, CA, USA), containing a mix of random primers and oligo(dT), according to the manufacturer's instructions. 2. 4. Quantitative Real-Time PCR (RT-qPCR) Methods and Data Analysis cDNA was diluted 1:20 in RNAse-free water. Pre-amplification was performed using PowerUp SYBR Green Master Mix (AB Applied Biosystems, Life Technologies, Woolston Warrington, UK).
Real-time quantitative PCR was carried out on a Bio-Rad CFX-96 detection system with quantitative qPCR SYBR Green reagents. Primers were designed to span intron/exon boundaries (preventing the amplification of contaminating genomic DNA) using Primer3 software (Whitehead Institute for Biomedical Research/MIT Center, Cambridge, MA, USA), while gene sequences were obtained from public databases (PubMed, Ensembl) (Table  1 ). PowerUp SYBR Green Master Mix (7. 5 µL) was mixed with 0. 3 µL of forward (10 µM) and reverse (10 µM) primer mix and 2 µL of diluted cDNA, and incubated at 95 • C for 10 min. PCR conditions were standardized to 40 cycles of: 95 • C for 15 s, 59 • C for 30 s and 65 • C for 5 s. Relative quantification of the target genes was conducted using eef1α housekeeping gene (caecum, jejunum).
Statistics All data are expressed as means ± SEM. Comparisons of data between D1, D14 and D56 in the fasted state for plasma metabolites concentrations and D14 and D56 for net nutrient fluxes were performed using a two way (time and diet effects) repeated measures ANOVA (SigmaPlot 12, Systat software, San Jose, CA, USA) followed by a post hoc analysis using the Tukey test. Comparisons between diets at D56 (data obtained in tissues) in the fasted state were obtained using a one-way ANOVA (SigmaPlot 12, Systat software, San Jose, CA, USA) followed by a post hoc analysis using the Tukey test. Comparisons between diets for the muscle fibre cross-sectional area was determined by Student's t-test calculated for each fibre-size range. Differences were considered significant if p < 0. 05 and as a tendency (t) for 0.
Overfeeding and Fibre Supplementation: Impact on Muscle Structure and Metabolic Activities Structural and phenotypic changes were observed at the muscle level (Longissimus dorsi) both after overnutrition and fibre supplementation. Of note, the increased average of muscle fibre CSA observed in animals from the O group is consistent with data already reported in obese humans  [39] [40] [41]  and Ossabaw pigs fed high fat/high cholesterol/high fructose  [42]  or Yucatan pigs fed a western diet  [43]. This increased fibre CSA positively correlates with obesity and could be explained by a higher number of intramyocellular lipid droplets, as previously shown  [42]. However, muscle triglycerides content remained unaltered after overfeeding in our study. The higher muscle fibre CSA in our O animals could be also due to a higher proportion of type II muscle fibres (lower size and glucose handling capacity) in detriment of highly-oxidative type I fibres, a feature associated with obesity in the literature  [42, 44].
Acknowledgments: To Cargill © for providing the dietary fibres (pectin, inulin and resistance starch). SCFAs analyses were performed within the  MetaboHUB French infrastructure  ( ANR-INBS-0010 ). The authors acknowledge  G Della-Valle  and  H Chiron  for bread making and design as well as  M. Jarzaguet ,  C. Buffière ,  J. P. Rigaudière ,  C. Buisson ,  E. Delmas  and the staff of Animal facility ( C. Delhomme ,  J. Hermet ) for technical assistance.
In this regard, one of the current successful approaches for the discovery of new molecules for biomedical research is the development of high-throughput methods of virtual screening for large databases of chemical compounds. 6 7 8 ere we report the development of a new patented analgesic molecule,  9  hereafter referred as thiowurtzine, and based on the hexaazaisowurtzitane (СL-20) molecule, a substance commonly known as a component of rocket fuel and explosives. Although several explosives such as nitroglycerin or potassium nitrate have been used as drugs in the medical history,  10 11 12 13  the case of thiowurtzine is quite different, as it is directly derived from the CL-20 molecule, but lacks all of the 6 nitro groups which confer its explosive properties to CL-20. As the 6 nitro groups are only added during the very last step of the chemical synthesis of CL-20,  14  thiowurtzine can very well be considered more as a modified precursor rather than a revised explosive molecule.
However, the antinociceptive effect of thiowurtzine was not completely abolished by naloxone, which suggests a complex mechanism of action of the compound under study. In these regards, the opioid analgesia pathway seems to be only one component of the mechanism of action of thiowurtzine, and consequently there may be other receptors targeted. In order to identify new potential targets for the action of thiowurtzine, we screened the Drugbank 27 database looking for structurally similar drugs and their wellknown receptors. In particular, we identified several drugs classified as calcium antagonists and acting as calcium channel blockers (namely pranidipine, nicardipine, and especially lacidipine, which exhibited the highest score of 86% for structural similarity with thiowurtzine). Lacidipine, like other dihydropyridines, targets the voltage dependent calcium channels, and has a selective activity on the Cav 1.
37 38  used the GOLD 39 (Genetic Optimization for Ligand Docking) software to perform docking experiments of the thiowurtzine molecule on the mu, kappa, and delta opioid receptors, the opioid like receptor 1 (ORL1), and on the voltage dependent calcium channel Cav 1. 2 as well The mu, kappa, delta and ORL1 opioid receptors were constructed using the corresponding UniProtKB human sequences and the I-TASSER software (Iterative Threading ASSEmbly Refinement) from the Zhang Lab. 40 The models were then checked, charged, and minimized with the MOE software using the AMBER 14:ETH force field. As no satisfactory structure was available for the mice calcium channel Cav 1. 2 interacting with a dihydropyridine, we also built a homology model using the Uni-ProtKB sequence Q13936 (isoform 1) of the human voltage-gated calcium channel subunit 1c Cav 1.
Docking of Thiowurtzine. The docking of thiowurtzine into the models of the five receptors (mu, kappa, delta, ORL1, and Cav 1. 2) was performed using the GOLD software. We used control drugs (tramadol, ketorolac, and diclofenac) as references. As tramadol is commercially available as a racemic mix (1R-2R and 1S-2S), we separately tested each of the two enantiomers and reported their respective score. All the results are summarized in Table  4. For the mu and delta receptors, the scores are about 2. 5 to 4 times lower than those of tramadol, ketorolac or diclofenac. However, the animal experiments evidenced that the doses of thiowurtzine needed to be typically 5 times higher than those of tramadol (20 mg/kg vs 100 mg/kg) and diclofenac (10 mg/kg vs 50 mg/kg) to produce similar effects with the acetic acid writhing test (Table  1 ).
A frame was saved every 200 ps, leading to a total number of 1,000 frames. The last frame of the simulation is displayed in Figure  2. The molecular complex formed by thiowurtzine and the Cav calcium channel model appeared to be extremely stable over time. In fact, thiowurtzine stayed within the interaction site during the 200 ns of the simulation, with only limited relative motions around its starting position at the beginning of the simulation. The full trajectory of the thiowurtzine mass center during the complete simulation is displayed with yellow sticks in Figure  2  and evidences a great stability of the complex over time. These results are in excellent agreement with the good docking scores previously obtained with the GOLD software for the thiowurtzine interacting with the model of the Cav receptor (Table  4 ).
40 This iterative modeling software can use several different partial templates when no single valid template is available. Five models for each receptor were computed using the software default options, and the five best ones were further imported into the MOE software to be checked, charged, and minimized using the AMBER 14:ETH force field. The homology model for the Cav 1. 2 calcium channel was built using the UniProtKB sequence Q13936 (isoform 1) of the human voltage-gated calcium channel subunit 1c Cav 1. 2 and the PDB structure 6JP5 of the rabbit Cav1. 1 / nifedipine complex (resolution 2. 90 Å) as a template. The alignment statistics were calculated as follows: 66. 4% identity, 75. 3% similarity, 4. 7% gaps for a total length of 1359 residues. Prior to the homology modeling, the geometry of all the molecules, bonds, and charges of the PDB structure 6JP5 were checked using the Structure Preparation tool of the Molecular Operating Environment software (MOE 2020, Chemical Computing Group, Köln, Germany) and minimized with the Amber 14:EHT forcefield implemented within the MOE software.
The homology model was generated using the 1C subunit as a template and the MOE standard parameters in the Protein / Homology Model module. Several loops (215-232, 446-509, 776-892, 941-962, and 1326-1374) were added as the corresponding residues did not exist in the template, but their structure and position were checked and none of them was involved in the interaction with nifedipine. Docking. The docking procedures were performed with the GOLD 39 software (version 2020. 3), using the HERMES  49  interface (CCDC, Cambridge Crystallographic Data Center) and the implemented GOLD Wizard. We used the GOLD standard parameters for the docking, with the exploration of a spherical site (10 Å radius) centered on the previously co-crystallized ligands. The Genetic algorithm (GA) parameter was set to a number of 200 poses with the activation of the early stop function.
We used the ASP (Astex Scoring Potential) fitness function  50  for calculating the docking scores, and the Chemscore scoring function  41 42  for the rescore of the results. Molecular Dynamics. Molecular Dynamics simulations were performed for the homology model of the Cav 1. 2 calcium channel in presence of thiowurtzine using the Amber 14:EHT force field implemented in the MOE software. A lipid bilayer surrounding the calcium channel and composed of DOPE (1,2-Dioleoyl-sn-glycero-3-phosphoethanolamine) and DOPG (1,2-Dioleoyl-sn-glycero-3-phosphorac-1-glycerol)-with a respective ratio of 3:1 was generated with the MOE lipid generator, using the PACKMOL-Memgen approach. 51 52 53 TIP3P water molecules  54 55  were added, as well as Na+ and Cl-ions in order to simulate a 0. 1 M ionic force, and pH was set at 7.
Tramadol 1S-2S 19,546 27,003 21,797 20,520 24,499 Tramadol 1R-2R 19,188 28,349 20,435 19,272 23,597 Ketorolac 19,529 26,150 23,640 19,467 22,065 Diclofenac 20,823 31,603 24,274 17,567 24,154 Thiowurtzine 8,465 2,730 6,407 2,153 15,562Note. The scores of the GOLD 39 docking software using the Chemscore41 42  rescore function are displayed. Interestingly, the docking scores of thiowurtzine are lower than those of the reference drugs for all the.
Instrumentation: SP2 BC concentrations were measured using an SP2 model B (retrofitted with photomultipliers for incandescence detection and the newer 8-channel data acquisition system) during the winter period and an SP2 model C during the summer period. As the same calibration and data analysis protocols were followed, no indication of a systematic difference in quantification was noted. The Droplet Measurement Technologies (DMT) SP2 (Boulder, Colorado) can quantify the mass of the refractory BC (rBC) within individual aerosol particles using laser-induced incandescence (LII) without any influence of non-rBC material  (Moteki and Kondo, 2007; Schwarz et al. , 2006). The laser beam operating at 1064 nm is used to measure rBC (hereafter referred to as BC) on a single particle basis. The laser heats up the particle, causing it to incandesce if BC is present.
Additionally, at ground level, the sample line was isokinetically divided into four flows using a TSI flow splitter. Two ports were connected to the pump; one was connected to an Aerodyne aerosol mass spectrometer (AMS), which deployed a further bypass pump providing an additional flow of about 1 L min -1 , and the other pulled air past the SP2 sampling inlet at a flow rate of ∼ 1 L min -1 , from which the SP2 subsampled at a flow of 0. 1 L min -1. This setup guaranteed the turbulent flow profile until just in front of the SP2 and made AMS and SP2 measurements as consistent as possible. The raw data recorded by the SP2 are the multi-channel data for individual particles; however only a subset of the particles detected is typically recorded, due to limitations imposed by hard drive space and computer throughput.
Since we were operating in a heavily polluted environment, we set the particle recording frequency to 1 in 30 for winter and 1 in 5 for summer. Ideally, every single particle (i. e. 1 in 1) would be analysed to maximise counting statistics and minimise uncertainty in the flux calculation, but limitations in data storage and processing power of the built-in computer prevented this. Count frequencies were based on the instrument performance and ambient concentrations, with concentrations higher in winter. During acquisition, the instrument handles data in 200 ms buffers, and each recorded particle is timestamped according to the buffer, so when the data are processed, it produces number and mass concentrations with a 5 Hz time resolution. If during acquisition a buffer is not fully processed before the next one has finished collection (which can occur at high concentrations), then a data buffer is discarded, which is manifested as a 200 ms gap in the data.
We use the widely used open-source software, EddyPro software version 6. 2. 1 (LI-COR Inc. ), to calculate the fluxes as used with CO 2 and other greenhouse gas flux measurements. A time lag occurs between wind and BC measurements from the spatial separation of the sonic anemometer and the SP2. Time lags can be determined for each 30 min period, but they can be difficult to quantify when the measurements have a low signal-to-noise ratio and fluxes are associated with higher uncertainty  (Langford et al. , 2015). Here, time lags are calculated using 24 h periods of 5 Hz data, with application of the covariance maximisation method  (Nemitz et al. , 2008; Su et al. , 2004)  giving a time lag for each day. As these are found to be consistent, a mean constant time lag of 13 (winter) and 14 s (summer) is used.
org/10. 5194/acp-21-147-2021 Atmos. Chem. Phys. , 21, 147-162, 2021 source characterisations. Firstly, here we compare the concentrations of BC at both levels. Figure  4a  shows the overlap periods of both measurements, and Fig. 4b  shows correlation between the two sampling heights with an R 2 = 0. 94 and 0. 70 for winter and summer, respectively. During winter, we observe a systematic difference between tower and ground measurement after the data gap on 25 November, corresponding to instrument maintenance for the ground-level SP2, which may have altered its performance. The difference was quantified by separating two periods and recalculating slopes comparing the tower and ground measurements as shown in Fig. 4c. From this, we can infer that the ground measurements increased by 24 %. Furthermore, for tower measurements, there were also particle line losses, and the upper limit of line losses would be 34 %.
(2017). The shape of the diurnal variation in the inventory was in better agreement; however, the BC / CO and BC / NO x ratios from the MEIC inventory were substantially larger than the measured ratios, which could indicate that an inaccuracy in the assumed BC emission profile is at least partially responsible for the disagreement. This indicates that BC emissions in the inventory are overestimated, at least in the urban area of Beijing. Data availability. Processed data are available in the APHH-Beijing project database in the Centre for Environmental Data Analysis Archive (http://data. ceda. ac. uk/badc/aphh/data/beijing/, Fleming et al. , 2017). Raw data are available on request. Competing interests. The authors declare that they have no conflict of interest. Special issue statement. This article is part of the special issue "In-depth study of air pollution sources and processes within Beijing and its surrounding region (APHH-Beijing) (ACP/AMT interjournal SI)".
Rutambhara Joshi's PhD was supported by the National Centre of Atmospheric Science (NCAS). The field measurements were supported by the Newton Fund, administered by the UK Natural Environment Research Council (NERC) through the AIR-POLL and AIRPRO projects of the Air Pollution and Human Health in a Chinese Megacity (APHH-Beijing) programme. Financial support. This research has been supported by the Natural Environment Research Council (grant nos. NE/N006992/1, NE/N006976/1, NE/N006917/1, NE/N007123/1 and NE/N00700X/1). https://doi. org/10. 5194/acp-21-147-2021 Atmos. Chem. Phys. , 21, 147-162, 2021.
Acknowledgements. Rutambhara Joshi's PhD was supported by the National Centre of Atmospheric Science (NCAS). The field measurements were supported by the Newton Fund, administered by the UK Natural Environment Research Council (NERC) through the AIR-POLL and AIRPRO projects of the Air Pollution and Human Health in a Chinese Megacity (APHH-Beijing) programme. Financial support. This research has been supported by the Natural Environment Research Council (grant nos. NE/N006992/1, NE/N006976/1, NE/N006917/1, NE/N007123/1 and NE/N00700X/1). https://doi. org/10. 5194/acp-21-147-2021 Atmos. Chem. Phys. , 21, 147-162, 2021.