dataesr/scientific-paragraphs-categorization huggingface dataset:
The NSL complex/BRD4 axis is conserved in mice and humans. Having identified the important role played by the NSL/BRD4 axis in transcription activation of Drosophila genes, we wondered whether it might also be conserved in mammals. Indeed, we had observed earlier that BET inhibitors impaired luciferase reporter activation by a tethered human KANSL3 (Fig. 3b, c ). Therefore, we performed comparative analyses of NSL complex and BRD4 binding profiles in mouse embryonic stem cells (mESC) using available ChIP-seq datasets  11, 42, 43. BRD4 showed strong binding to NSL-occupied promoters in mESCs (Supplementary Fig. 6a ). In addition, BRD4 binding patterns appeared overall very similar between three different cell types of different developmental stages: ESCs, Embryoid Bodies (EB) and mixed lineage leukemia (MLL) cells (Supplementary Fig.
6e ). Consistent with the housekeeping gene regulatory function of the NSL complex, these pathway analyses likely reflect an overall imbalance of cellular and metabolic homeostasis in KANSL1 haploinsufficient patients. We then compared Koolen-de Vries syndrome expression profiles to JQ1-treated MOLT4 cells  33  (also see Supplementary Fig. 3e ). We chose this JQ1 dataset, because it contained ERCC spikes for normalization. We classified genes based on their response to JQ1 into JQ1-up, JQ1-down or JQ1-not DE. We then looked at the response in these three groups of genes in the Koolen-de Vries patient fibroblast dataset. We observed that the JQ1-down group is predictive of a downregulation in the patient cells (p-value = 1. 5e -49 vs. not DE group, Fig. 6f , Supplementary Fig. 7c ). This overlap of BET and KANSL signatures was also evident when we classified transcriptome alterations in patients according to directionality of the change (up-or downregulation).
a Heatmaps and profile plots of dBRD4-S-Biotin ChIP-seq in control (GST RNAi), NSL1 RNAi, MOF RNAi and WT untagged cells. The upper cluster comprises 2491 dBRD4-S-Biotin-bound and endogenous dBRD4-bound genes, the lower cluster of a similar number of randomly selected, not dBRD4-S-Biotin-bound genes. Both clusters are ordered by average intensity of all four ChIP datasets. Input-subtracted ChIP-seq data (merge of two biological replicates) is shown. b Boxplot of dBRD4-S-Biotin ChIP signal (log2 fold change over input) (merge of two biological replicates) on promoters of dBRD4-S-Biotin-bound genes (n = 2491) (±200bp from TSS) for the respective RNAi experiment. Boxplots show median (centre), interquartile-range (box) and minima/maxima (whiskers). Twosided Welch two sample t-test was applied.
Cloning. Plasmids were generated by PCR-based restriction cloning. PCR amplification was performed using PrimeSTAR GXL DNA Polymerase (Takara). For dBRD4-S-stable cell line, cDNA encoding for dBRD4-S was PCR amplified from the Drosophila Gold Collection clone LD26482 (available though DGRC), using primers with PacI and AscI restriction site overhangs (see Supplementary Data 3) and cloned into a modified pCo-Blast vector (Thermo Fisher K5150-01), containing a copper inducible MtnA promoter and a 3xFLAG-6xHis-Bio-6xHis cassette for C-terminal tagging. For recombinant dBRD4-S expression, dBRD4-S (aminoacids 1-1063) was amplified from cDNA as for dBRD4-S stable cell line (see above), using primers with BamHI and HindIII restriction sites (see Supplementary Data 3) and cloned into a customized pET28 vector containing a 6xHis-SMT3 cassette for N-terminal tagging.
Drosophila husbandry. Drosophila melanogaster were reared on a standard cornmeal fly medium at 25 °C, 70% relative humidity and 12 h dark/12 h light cycle. Experimental crosses for dBRD4-L-HA fly lines were conducted at RT. The following stocks were used: w 1118 ;P{da-GAL4. w-}3 (Bloomington stock #8641) w;; UAS-Fs(1)h. L::HA (Bohmann laboratory) RNAi in Drosophila S2 cells. Production of dsRNAs was performed in the following way: DNA template for in vitro transcription was generated by PCR (primers as listed in Supplementary Data 3), T7 in vitro transcription was carried out using HiScribe T7 High Yield RNA Synthesis Kit (NEB) according to manufacturers' instruction. RNA cleanup was performed using MEGAclear Transcription Clean-Up Kit (Ambion). in all, 10 µg dsRNA were used per 1 × 10 6 cells.
Cells were harvested after 4 days. This method was adapted from a previously described method  59. Drug treatments. Drug treatments were performed by adding the respective reagent to the cell culture medium; timings as indicated in the figure legends. The following inhibitors were used: JQ1 (1187, Cayman Chemicals, 5 µM), iBET 762 (10676, Cayman Chemicals, 1 µM), dBET6(Gray lab, 100 nM), Panobinostat LBH-589 (404950-80-7, Biozol Diagnostica, 200 nM). RNAi screen. HD2. 0 library of dsRNAs was used. This library was designed using the software NEXT-RNAi  18  , excluding low complexity regions and sequence motifs inducing off-target effects. 14000 protein coding genes and 1000 non-coding genes are targeted. The genome-wide RNAi screen was performed with two technical replicates. Per well 1. 4 × 10 4 cells were seeded in 384-well plates prespotted with 250 ng dsRNAs each.
After 24 h cells were transfected with a plasmid mix of 3 ng Renilla reporter (pRL-hsp70), 17. 5 ng UAS-firefly reporter (pG5luc) and 0. 87 ng NSL3-Gal4 DBD activator (pAct5. 1-NSL3-Gal4-DBD)  15 per well using effectene transfection reagent (Qiagen) according to manufacturer's instructions. 3d post transfection, cells were lysed and subjected to dual luciferase assay readout by chemiluminescence with a Mithras LB940 plate reader (Berthold Technologies). Each assay plate contained wells with positive control dsRNAs (NSL complex members: NSL1, NSL3 and MOF), and negative control dsRNAs (GFP, GST, Diap-1). Data were analysed using the online software CellHTS2   1g ) were excluded from further analysis and also from the subset library tested in the secondary screen. Secondary screen was performed like genome-wide screen, but with a subset library consisting of 367 candidates, 302 with Z-score < -7.
1% Tween, then incubated with primary antibodies overnight at 4 °C. Secondary HRP-coupled antibodies were used at 1:10 000 dilution for 1 h. Blots were developed using Lumi-Light Western Blotting substrate (Roche) and imaged on a ChemiDoc XRS + machine (Biorad). Following primary antibodies were used: dBRD4 (Paro lab, ID166, 1:1000), Pol2 ser2p (ab5095, Abcam, 1:5000), Pol2 ser5p (ab5131, Abcam, 1:5000), Rpb3 (Akhtar lab, 1:1000), H3 (ab10799, Abcam, 1:3000), NSL3 (Akhtar lab, 1:1000), MOF (Akhtar lab, 1:3000), H2A. V (61752, Active Motif, 1:2000), MCRS2 (Akhtar lab, 1:3000), H4K16ac (07-329, Millipore, 1:3000), KANSL2 (HPA038497, Sigma, 1:1000), BRD4(A301-985, Bethyl, 1:2000), ACTIN-HRP(sc-1616, Santa Cruz, 1:5000), GAPDH-HRP (MA5-15738, Thermo Scientific, 1:5000), FLAG-HRP(A8592, Sigma, 1:5000).
5-1 µg of total RNA, for mESC cDNA 10% Drosophila total RNA were added prior to RT reaction. Expression of target genes was normalized to robustly not changing genes in RT-qPCR experiments (HPRT for mESC, ATPsynbeta, sun and SP1029 for Drosophila cells) Quantitative real-time PCR. qPCR was performed using FastStart Universal SYBR Green Mastermix (Roche) on a Roche LightCycler 480 with 300 nM final primer concentration in 7 µl reaction volume. We corrected for primer efficiency using serial dilutions. RNA-seq analysis. For Drosophila samples, reads were mapped to the Drosophila genome (dm6) using subreads  61  , transcripts were counted with htseq  62  and differential expression analysis was performed using DESeq2  63. Here ERCC spike normalization was performed. ERCC spikes were added according to total RNA amount per sample before library preparation.
Spikes were mapped using STAR  64  (default parameters) and counted using feature counts  65  (default parameters). DESeq2 on spike counts (again of three biological replicates per sample) was used to calculate the size factor, which was used to normalize gene expression DESeq2. Expression analysis by qPCR of another independent replicate was performed to validate the RNA-seq results. RNA seq from fibroblast samples was processed using Snakepipes. Reads were mapped to hg38 genome using STAR (default parameters) and transcripts were counted using featureCounts. Differential expression analysis was performed with DESeq2 63 , comparing healthy control samples (n = 4) to patient samples (n = 3). Pathway analysis for patient fibroblast samples were performed using Ingenuity Pathway Analysis (Qiagen).
Acknowledgements We thank  Claudia Blass  and  Barbara Schmitt  for technical assistance and  Ulrike Hardeland  for advice in performing the high-throughput screening. We thank  Renato Paro  for kindly sharing dBRD4 antibodies with us,  Nathanael Gray  and  Stirling Churchman  for providing support concerning the dBET compound and  Dirk Bohmann  for sharing the UAS-dBRD4-L-HA fly line. We thank members of the  MPI-IE  facilities for support. We thank  Claudia Keller Valsecchi  for help with biolayer interferometry experiments. We thank  Plamen Georgiev  and  Anastasios Alexiadis  for help with fly work and  Nhuong Nguyen  for generating mammalian luciferase constructs. We thank  Devon Ryan ,  Fidel Ramirez ,  Friederike Duendar ,  Olga Bondareva  and  Gina Renschler  for their support in bioinformatics analyses and  Sukanya Guhathakurta  for help with mESC generation and fibroblast cell culture.
Data availability RNA-seq and ChIP-seq data have been deposited to Gene Expression Omnibus (GEO) under the accession number: GSE135815 and are also accessible under BioProject PRJNA560185. All other relevant data supporting the key findings of this study are available within the article and its Supplementary Information files or from the corresponding author upon reasonable request. The source data underlying Figs. 2a, 3b-d, 4d-g, 5b, 6a,b and Supplementary Figs. 1a, b, 3a-c, 4b, 4g,h, 5f,g, 6c-e, 7a,b are provided as Source Data files.
Comparison with Some State-Of The-Art Evolutionary Algorithms In this section, we choose three well-known multiobjective evolutionary algorithms NSGA-II, PESA-II, and MOEA/D (MATLAB implementation obtained for the yarpiz library available at www. yarpiz. com. ). The Tchebychev function in MOEA/D was selected as the scalarizing function and the neighborhood size was specified as 15% of the population size. The population size in the three algorithms was set to 100, The size of the archive was set to 50 in PESA-II and MOEA/D. Besides the 14 bi-objective considered problems in the previous section, 4 additional 3d objective problems will be also tested, which are DTLZ1, DTLZ2, DTLZ3, DTLZ4. However, note that, as the TM decomposition is not suitable in the 3d cases, those 3d problems will be tested with X-Tornado-TS variant.
Also,  [26]  investigated via simulations and experimentally an indoor VLC channel considering random movements of people, concluding a Rayleigh distribution for the channel gain. A distance-dependent VLC path loss model was presented in  [27]  for a user moving along different trajectories in a furnished indoor environment. Lastly,  [28]  studied the influence of random device orientations on the statistics of the received signal-to-noise (SNR) for a mobile user. A few studies have considered channel characterization for optical WBANs in the IR band such as the work in  [29] , which was based on MCRT simulations using a dedicated software, called RapSor  [30]. On the other hand, channel characterization for extra-WBAN links (i. e. , between a CN and an AP located in the room) was considered in  [31] -  [34] , where user mobility was considered based on uniformly distributed random user positions and orientations of the sources/detectors.
C. CHANNEL CHARACTERIZATION In this work, we use the non-sequential ray-tracing feature of Opticstudio from Zemax  [47] , which is a licensed optical design software for MCRT simulations. The advantage of Opticstudio is its ability to model complex 3D environments. The ray-tracing output consists of a detailed database file of the generated rays, including their power and optical path length after each reflection. The CIR, h(t), can then be estimated as follows. h(t) = 1 N rays N rays i=1 P r,i δ t - d i c , ( 9 ) where c is the speed of light and d i is the total distance that the i th ray has traveled before reaching the detector. We use several metrics to analyze the obtained simulation results, including the channel DC gain and delay spread. The DC gain H 0 is defined as  [48] : H 0 = h(t)dt.
III. NUMERICAL SIMULATION OF INTRA-WBAN LINKS A. SIMULATION METHODOLOGY The block diagram of our simulation methodology is given in Fig. 4. The first step consists in building the 3D scene inside the Opticstudio simulator. The 3D objects are either created from built-in objects or imported using a 3D computer-aided design (CAD) software such as Blender  [50]  (Block 1 in Fig. 4 ). We then define the reflective properties of these objects (e. g. , the BRDF of the different surfaces), as well as the properties of the sources and detectors, i. e. , Tx radiation pattern, detectors field-of-view (FOV), detector area, etc. (Block 2 ). By adding up these 3D objects, we construct the environment and its constituent reflectors (Block 3 ). The next step is to include the mobility model (Block 4 ). We use a Python script to load a set of N config configurations describing objects' movements and orientations during the consecutive snapshots of a mobility scenario.
C. USER MOBILITY MODELING To account for user mobility, we should consider at the same time the local mobility, which concerns body parts movements, and the global mobility, which concerns the movements of the whole body inside the room. We describe the local mobility of the body by a 3D animation of a walk cycle created (manually) using the Blender software  [50]. Guidlines for creating the animation can be found in  [54]. The animation consists of a set of N f = 27 frames, representing the body at different instants of the walk cycle,  as illustrated in Fig. 6. 2  Note that as the result of this body movement, the positions and orientations (i. e. , angles φ and θ ) of the SNs will change since the body configuration is different from one frame to another. To account for these changes, we use  (15)  to calculate the new SNs' positions and orientations in each frame (based on the position and orientation of the corresponding faces on the mesh object).
V. CONCLUSION We investigated channel modeling and characterization for optical intra-WBANs links, i. e. , between a set of SNs and a CN, during a walk scenario. The presented study provides useful insights into the effects of local and global user mobility and the corresponding beam shadowing. These effects were investigated using a realistic dynamic model based on a 3D animation of the walk cycle and a modified RWP mobility. MCRT-based simulations were then done using the Opticstudio software to obtain the CIRs for a set of SN and CN configurations at different instants of the walk scenario. The considered configurations cover the most likely locations of the CN and the SNs for a practical implementation of a medical WBAN. We presented comprehensive numerical results on the channel first-and second-order statistics, namely the DC gain, delay spread, ACF, and coherence time.
Based on the datasets obtained from simulations, we proposed best-fit models to describe the statistical distributions of the channel parameters, which are particularly helpful for studying the channel behavior (and consequently, the link performance) for physical or upper layer design. LN and Gamma distributions were found to provide the best AIC fit models for the channel attenuation. Lastly, regarding channel time variations, optical intra-WBAN links were shown to have a slower time-varying nature (thus, of higher stability), as compared to their RF counterparts; the corresponding coherence times ranging between a few hundred milliseconds to a few seconds. Note that the presented study was based on considering the typical body shape and dimensions of an adult person. The choice of body shape will determine the propagation of the rays in the vicinity of the human body.
(C) Amperometric spike parameters analyzed in chromaffin cells of wild-type (control), Pld1 À/À , and Pld2 À/À mice. PLD1 knockout affected foot duration and three spike parameters, namely, spike rise time, spike half-width, and spike amplitude (I max ). Data are expressed as means ± SD (n > 100 cells for each condition from four independent cell cultures; *p < 0. 05, **p < 0. 01, ***p < 0. 001 compared with control). (D) PLD1 activity is required for CgA secretion. Bovine chromaffin cells in culture were treated with the PLD inhibitors FIPI, CAY93 (C93), CAY94 (C94), or vehicle (C) for 1 h before stimulation for 10 min with 59 mM K +. Secretion of CgA was estimated by western blot quantification of the CgA present in the cell incubation medium from resting (R) and stimulated (S) cells. Data are given as the mean values ± SD obtained in three experiments performed on cell cultures (n = 3; **p < 0.
This apparent discrepancy with our previous results might be linked to the presence of residual PLD1 in siRNAtreated cells compared with genetically modified Pld1 À/À cells (B) Bovine chromaffin cells were treated with 750 nM FIPI or vehicle (control) for 1 h and incubated with 100 mM of the PA mix or 10 mM of the indicated PA species for 15 min before stimulation with 10 mM nicotine. Net catecholamine release was obtained as in (A). Data are given as the mean values ± SD obtained from three independent experiments performed on cell cultures (n = 3; ***p < 0. 001 compared with control). (C) Chromaffin cells were treated with 750 nM FIPI or vehicle (control) for 1 h and incubated with 100 mM of the indicated PA for 15 min before stimulation with a local application of Locke's solution containing 100 mM K + for 10 s.
Single-cell secretion was monitored by carbon fiber amperometry. Data are expressed as means ± SD (n > 75 cells for each condition from three independent cell cultures). Mono-unsaturated PA rescues the number of amperometric events, whereas poly-unsaturated PA restores spike parameters (***p < 0. 001 compared with control). (Zeniou-Meyer et al. , 2007). Hence, local and timely accumulation of PA produces distinct membrane trafficking and signaling effects that might define pleiotropic functions for PA in regulated exocytosis  (Tanguy et al. , 2019a). These include the recruitment of proteins involved in priming or even fusion events; the stimulation of phosphatidylinositol 4,5-bisphosphate (PI(4,5)P 2 ) production, a lipid well known for its role in the priming steps of exocytosis (Martin, 2012); and as previously proposed, the promotion of negative curvature in lipid bilayers generating physically fusion-competent membranes  (Martin, 2012) , as well as many steps of vesicular trafficking in neurosecreting cells  (Di Paolo and De Camilli, 2006).
Interestingly, using super-resolution microscopy (A) Bovine chromaffin cells were incubated with 750 nM FIPI or vehicle (control) for 1 h to inhibit PLD1. Cells were then stimulated for 5 min with 59 mM K + or maintained under resting conditions. Electron micrographs of plasma membrane sheets from resting or stimulated chromaffin cells treated or not with FIPI are shown. Scale bar, 500 nm. (B) Bovine chromaffin cells were incubated for 15 min with 100 mM of the indicated PA in the presence or absence of 750 nM FIPI, before being stimulated for 5 min with 59 mM K + or maintained under resting conditions. The number of granules morphologically docked on plasma membrane sheets was quantified manually on random fields of 45 mm 2. Data are presented as means ± SEM (n > 60 images from three cell cultures for each condition; ***p < 0.
R. Borges laboratory; http://rborges. webs. ull. es/) written for Igor Pro software (WaveMetrics), allowing automatic spike detection and extraction of spike parameters. The number of amperometric spikes was counted as the total number of spikes with an amplitude > 5 pA within 60 s. The spike parameters analysis was restricted to spikes with amplitudes > 5 pA. Quantal size (spike charge) of individual spike is measured by calculating the spike area above the baseline. Spike area is defined as the time integral of each current transient, I max the height of each peak, and half-width the width of each peak at half its height (Figure  2C ). All peaks identified by the program were visually inspected and overlapping peaks were manually excluded from the datasets. For foot signal, the analysis was restricted to spikes with foot amplitudes > 1.
, 2019). The fluorescence emitted by adrenolutin (l ex : 430 nm, l em : 520 nm) is measured with a spectrofluorometer (LB940 Mithras, Berthold). For each experiment a standard curve was determined using known concentration of adrenaline and noradrenaline to demonstrate that the values obtained were in the linear range of detection of the assay. Amounts released were expressed as a percentage of the total amount of catecholamine present in the cells. Plotted data are representative of at least three independent experiments, each carried out in triplicate. For CgA secretion assay, bovine chromaffin cells were cultured in 24-wells plate for 48-72 h and then incubated for 60 min with the indicated PLD inhibitors, prior to stimulation in the presence or absence of the PLD inhibitors. 50 mL supernatants were collected and added to 10 mL of 10 x Laemmli buffer.
Net catecholamine release was obtained as in (A). Data are given as the mean values ± SD obtained from three independent experiments performed on cell cultures (n = 3; ***p < 0. 001 compared with control). (C) Chromaffin cells were treated with 750 nM FIPI or vehicle (control) for 1 h and incubated with 100 mM of the indicated PA for 15 min before stimulation with a local application of Locke's solution containing 100 mM K + for 10 s. Single-cell secretion was monitored by carbon fiber amperometry. Data are expressed as means ± SD (n > 75 cells for each condition from three independent cell cultures). Mono-unsaturated PA rescues the number of amperometric events, whereas poly-unsaturated PA restores spike parameters (***p < 0. 001 compared with control).
ACKNOWLEDGMENTS We acknowledge the  Plateforme Imagerie In Vitro of Neuropôle  at  CNRS UPS-3256 , the municipal slaughterhouse of  Haguenau (France)  for providing the bovine adrenal glands, and the animal facility of  Institut des Neurosciences Cellulaires et Integratives (Chronobiotron UMS 3415 ) for handling mice. We also thank  Dr. B. Nieswandt  ( University of Wurtzburg ) and  Dr. G. Di Paolo  ( Denali Therapeutics ) for providing Pld1 and Pld2 mice lines, respectively. This work was supported by grants from the  Fondation pour la Recherche Medicale  ( DEI20151234424 ) and the  Agence Nationale pour la Recherche  ( ANR-19-CE44-0019 ) to N. V.
However, using finite element analysis or numerical methods to simulate the coating deposition process, such as ANSYS and MATLAB, it always requires meshing the workpiece, and then calculates the coating thickness and assigns the final result to each node of the grid. As the nozzle moves, the coating thickness of each node will be continuously updated. Therefore, this approach must be based on the same dimension and continuous grid and cost much computation. For those substrates with a large radius of curvature or discontinuous surface regions, it would not be suitable. Based on the reviewed works above, it is easy to conclude that most of the coating-thickness simulations mainly focus on the development of coating-thickness models. The experimental process is generally an operation of numerical model superposition, for which it is difficult to show the behavior of coating stack and the macroscopic appearance of final coating distributions intuitively.