dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was partially supported by  Grants-in-Aid for Scientific Research  ( JSPS KAKENHI  Grant Number  JP25104011 ). The SPring-8 synchrotron radiation experiment was performed on BL40B2 with the approval the  Japan Synchrotron Radiation Research Institute  (JASRI; proposal no. 2011B1548 ). The authors thank the beamline contact ( N. Ohta ,  SPring8 ) for his help with performing SAXS measurements. We thank the  ILL  for the beam time on the D11 instrument. The neutron data set is available at DOI:  10. 5291/ILL-DATA. 9-10-1422. 12MMB  and CFS thank the  EPSRC  (grant  EP/K039245/1 ) and  University of Birmingham  for financial support. AD thanks the  University of Birmingham  and the  EU (Marie Curie ''SusNano'')  for his PhD studentship. Finally, STFC is acknowledged for the allocation of beam time, consumables and travel.
The AMP rational design methods could be split in two main categories, in cerebro design and computer-aided design  11  , both of which have successfully been used to generate synthetic AMP sequences. However, both strategies are strongly influenced by the information encoded in AMP sequences deposited in databases, which limits their capacity to identify unknown AMP sequences beyond those described in the literature. In cerebro design methods rely on the bacterial membrane as a target for AMPs; in practical terms, this kind of design approach creates and/or modifies peptide sequences by means of increasing peptide cationicity and hydrophobicity, mainly by inserting lysine, leucine, and alanine residues within the sequence, thus enhancing the interaction between peptide and membrane  12, 13.
Computeraided design methods make it possible to explore the peptide sequence space of AMPs using a number of algorithms. Unfortunately, the optimal solutions of such approaches end up sharing approximately 40% identity with AMP sequences deposited in the databases  [14] [15] [16]  , converging on a relatively small portion of AMP sequences composed of a restricted set of amino acids  17, 18. Even when incorporating non-proteinogenic amino acids into AMP sequences, for instance by exchanging cationic or hydrophobic residues for ornithine or norleucine residues, respectively  15, 19  , this approach fails to identify AMP sequences with unique amino acid composition. Thus, known AMPs show redundancy in their primary sequence, suggesting that only a little parcel of the combinatorial sequence space (20 x , x being the number of residues in a peptide chain) have been exploited.
5% of probability of mutation (Fig. 1b ). As shown in Fig. 1c , the fitness values for the population and for the best sequence were improved without reaching stabilization and with no hits in CAMP databases, at 50th iteration, indicating a suboptimal solution and a different composition. The final set was composed of the best sequence of each parallel run, comprising peptides with fitness values ranging from 0. 245 to 0. 393, named guavanins 1-100 (Supplementary Table  1 ). The amino acid composition of all the guavanins was different from other AMPs deposited in the Antimicrobial Peptides Database (APD), even taking into account only those peptides assigned with an α-helical structure (Fig. 1d ), and despite guavanins are Arg-rich peptides, it draws attention the fact that they use Tyr residues as the hydrophobic counterpart (Fig.
c Fitness function evolution during the algorithm iterations and the number of CAMP hits of the highest ranked sequence at iterations 50, 100, 200, and 400; Guavanin sequences were retrieved from the 50th iteration. d Amino acid distribution of guavanins and AMPs from APD2 and PhytAMP. Blue squares represent data obtained from 100 guavanin sequences; orange diamonds, the top 15 guavanins; yellow down triangles, the overall APD2 composition; green up triangles, the composition of α-helical peptides from APD2; and brown right triangles the plant AMP sequences from PhytAMP (37). e The frequency logo of the 100 generated guavanin sequences (Supplementary Table  1 ), showing that they are arginine-rich peptides, Arg residues are in at least 20% of their compositions shown in Fig. 2b , a rapid and maximal SG fluorescence signal was reached after incubation of bacteria with 5 µM of melittin, a 26residue AMP from bee venom that acts on bacterial membranes via pore formation  25  and serves as a positive control for membrane damage.
Discussion The first plant AMPs were identified in the 1970s and since that time a range of different classes of AMPs have been identified  5. Overall, they are composed of tens of amino acids residues, have an uncommon composition and structures stabilized by disulfide bridges. The complexity of their chemical structures is perhaps the main disadvantage of plant based AMPs and likely contributes to why none have reached the market  5. Promising design methods have recently been applied to engineer AMPs and help overcome such limitations while simultaneously increasing AMP potency and reducing cytotoxicity towards human cells  10. Unfortunately, many of these methods are based on incremental modifications of an AMP template and when peptides are designed from scratch they often share similarity with AMP sequences found in databases and consequently only a very limited set of amino acids are harnessed to design the synthetic AMPs.
The sequence with the highest fitness values, among all independent simulations, at iterations 50, 100, 200, and 400 was subjected to a BLAST search  42  against the CAMP database 43 in order to select the iteration that generated sequences which lacked similarity to known AMPs. Finally, the best sequence from each independent simulation at the 50th iteration was chosen and then the set was ranked; the 15 best sequences, according to the fitness function, were selected for further evaluation. Fitness function. The Eq. (  1 ) was designed to generate amphipathic α-helical peptides, based on the ratio between Eisenberg's hydrophobic moment and the  sum of exponential α-helix propensity in Pace-Schols scale: Fitness ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi P I i¼1 H i cosðδiÞ Â Ã 2 þ P I i¼1 H i sinðδiÞ Â Ã 2 2 q P I i¼1 e Hxi ; ð1Þ where δ represents the angle between the amino acid side chains (100°for α-helix, on average); i, the residue number in the position i from the sequence; H i , the ith amino acid's hydrophobicity on a hydrophobicity scale; Hx i , the ith amino acid's helix propensity in Pace-Schols scale  44  ; and I, the total number of residues present in the sequence.
albicans, as determined by growth curves. Biofilm cells were stained using Syto-9 prior to microscopy experiments. Microscopy was done using a confocal laser scanning microscope (Olympus, Fluoview FV1000). Imaris software package (Bitplane AG) was used for three-dimensional reconstructions and quantifications of the overall biofilm volume (µm  3  ) and the percentage of live and dead cell volume. Experiments were performed at in triplicate. Cytotoxic profiles. We determined the cytotoxicity of guavanin 2 against the human embryonic kidney cell line HEK-293. HEK-293 cells were cultured in DMEM medium, and incubated at 37 °C in a humidified atmosphere of 5% CO 2. Cell viability was quantified after peptide incubation using a methylthiazolyldiphenyl-tetrazolium bromide (MTT)-based microassay  55.
Briefly, cells were seeded on 96-well culture plates at a density of 5 × 10 5 cells mL -1 and incubated 72 h at 37 °C with 100 µL of guavanin 2 at different concentrations (12. 5-200 µM, final concentrations). Then, 10 µL of MTT (5 mg mL -1 in PBS) was added to each well and the cells were further incubated for 4 h in the dark. The formazan crystals formed by mitochondrial reductases in intact cells are insoluble in aqueous solutions and precipitate. Formazan crystals were dissolved using a solubilization solution (40% dimethylformamide in 2% glacial acetic acid, 16% sodium dodecyl sulfate, pH 4. 7) followed by 1 h incubation at 37 °C under shaking (150 rpm). Finally, the absorbance of the resuspended formazan was measured at 570 nm. Data were analyzed with GraphPad Prism® 5. 0 software to determine the inhibitory concentration 50 (IC 50 ), which corresponds to the peptide concentration producing 50% cell death.
CD spectroscopy. CD assays were carried out using JASCO J-815 spectropolarimeter equipped with a Peltier temperature controller (model PTC-423L/15). Measurements were recorded at 25 °C and performed in quartz cells of 1 mm path length between 195 and 260 nm at 0. 2 nm intervals. Six repeat scans at a scan-rate of 50 nm min -1 , 1 s response time and 1 nm bandwidth were averaged for each sample and for the baseline of the corresponding peptide-free sample. After subtracting the baseline from the sample spectra, CD data were processed with the Spectra Analysis software, which is part of Spectra Manager Platform. Spectra were obtained in the following conditions, water (pH 7), sodium dodecyl sulfate (20 mM; pH 4, 7 or 10), dodecylphosphocholine (20 mM; pH 4) and 2,2,2-Trifluoroetanol 50% (v/v; pH 4).
The peptide concentration was 33 and 38 μM for water and other systems, respectively. The relative helix content (H) according to the number of peptide bonds (n) was calculated from the ellipticity values at 222 nm  60. NMR spectroscopy and structure calculations. The NMR sample was prepared by dissolving guavanin 2 in a micellar solution containing 100 mM of deuterated dodecylphosphocholine (DPC-d 38 ) and 5% D 2 O at 1 mM concentration. The pH was adjusted to 4. 0. All spectra were acquired at 25 °C on a Bruker Avance III 500 spectrometer equipped with a 5 mm triple resonance broadband inverse (TBI) probehead. Proton chemical shifts were referenced to sodium 2,2-dimethyl-2silapentane-5-sulfonate (DSS) and water suppression was achieved using the presaturation technique. 1 H-1 H TOCSY experiment was recorded with 128 transients of 4096 data points, 256 t1 increments, and a spinlock mixing time of 80 ms.
The 1 H-1 H NOESY was recorded with 64 transients of 4096 data points, 256 t1 increments, mixing time of 250 ms. Spectral width of 8012 Hz in both dimensions. 1  H-  13  C HSQC experiment was acquired with F1 and F2 spectral widths of 8012 and 25152 Hz, respectively, were collected 256 t1 increments with 96 transients of 4096 points for each free induction decay. The experiment was acquired in an edited mode. All NMR data were processed using NMRPIPE  61  and analyzed with NMR View  62. The structure calculations were performed with the XPLOR-NIH version 2. 28 software by simulated annealing (SA) algorithm  63. NOE intensities were converted into semi-quantitative distance restrains using the calibration by Hyberts et al. 64. The angle restraints of phi and psi of the protein backbone dihedral angles were predicted based on analysis of 1 H α and 13 C α chemical shifts using the program TALOS+  65.
Several cycles of XPLOR were performed using standard protocols. After each cycle rejected restraints, side-chain assignments, NOEs, and dihedral violations were analyzed. Two hundred structures were calculated, and among them, the 20 lowest energy structures were submitted to XPLOR-NIH water refinement protocol  63. The ensemble of the 10 lowest energy conformations was chosen to represent the solution structure ensemble of guavanin 2. The restrictions used in structural calculations were analyzed by QUEEN program (Quantitative Evaluation of Experimental NMR Restraints). This program performs a quantitative assessment of the restrictions of the experimental NMR data. QUEEN checks and corrects possible assignments of errors by the analysis of the restrictions  66. The stereochemical quality of the lowest energy structures was analyzed by PROCHECK  50  and ProSA  49.
PROCHECK was used in order to check stereochemical quality of protein structure through the Ramachandran plot, where good quality models are expected to have more than 90% of amino acid residues in most favored and additional allowed regions. ProSA indicates the fold quality by means of the Z-score. The display, analysis, and manipulation of the threedimensional structures were performed with the program MOLMOL  67  and PyMOL (The PyMOL Molecular Graphics System, Version 1. 8 Schrödinger, LLC). Solvation potential energy calculation. The solvation potential energy was measured for the ten lower energy NMR structures. Each structure was separated into a single pdb file. The conversion of pdb files into pqr files was perfomed by the utility PDB2PQR using the AMBER force field  68. The grid dimensions for Adaptive Poisson-Boltzmann Solver (APBS) calculation were also determined by PDB2PQR.
Acknowledgements This work was supported by  CNPq  ( Conselho Nacional de Desenvolvimento Científico e Tecnológico) ;  CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) ;  FAPDF (Fundação de Amparo a Pesquisa do Distrito Federal) ;  FUNDECT (Fundação de Apoio ao Desenvolvimento do Ensino, Ciência e Tecnologia do Estado de Mato Grosso do Sul) ;  Ramon Areces Foundation  (to C. F. -N. ) and  DTRA  ( DTRA HDTRA1-15-1-0050 ) (to T. K. L. ). The authors wish to thank  Fabio C. L. Almeida  for assistance with the structure statistics.
We crossed Nr1h3 fl/fl mice with Clec4f-cre mice generating Clec4f-crexNr1h3 fl/fl mice. This confirmed that loss of Nr1h3 did not affect the proportion or absolute number of total hepatic CD64 + F4/80 + macs (Figure  4A ). However, as for the loss of Zeb2, it altered the proportions of cells expressing Clec4F and Tim4 (Figure  4B ). Protected chimeras demonstrated that in the absence of Nr1h3, KCs were being replaced from a BM source (Figure  4C ). As these data suggest that ZEB2 might function to control KC identity through maintaining LXRa expression, we next determined whether the effect of Zeb2 loss on the KCs transcriptome might reflect loss of LXRa-dependent genes. Thus, we performed SC-RNA-seq of KCs from Clec4f-crexNr1h3 fl/fl mice and Nr1h3 fl/fl littermate controls. Following the same preprocessing as above (Figure  S1G ), we identified 2 main groups of KCs in the t-SNE plot of CRE À and CRE + cells.
As loss of ZEB2 and LXRa led to replacement of the KCs from the BM, we noticed that a number of the DE genes were also related to origin of the KCs. To remove any DE genes associated with mac origin and hence only examine DE genes resulting from the loss of LXRa or ZEB2, we used our previously published data  (Scott et al. , 2016b)  and identified any DE genes between moKCs from KC-DTR mice 15 days post treatment with DT and embryonic KCs (Figure  S4D ). Comparison of the overlap between the remaining non-origin related DE genes associated with the Zeb2 À/À and Nr1h3 À/À KCs identified that 203 of the 435 DE genes in Nr1h3 À/À KCs were conserved in both datasets (Figure  4H ), including many of the liver-specific core KC genes. Crucially, while there is overlap between the two genotypes, it is not 100%, indicating that loss of LXRa is not solely responsible for all the DE genes identified in Zeb2 À/À KCs.
As for the liver and lung, this revealed that the core profiles of the different macs were altered in the absence of Zeb2 (60% in SMs and microglia and 76% in CMs; Figures  7A-7C , with indicated groups from Figure  6 ). Consistent with the data from the liver and lung (Figures  2G-2I ), Zeb2 À/À SMs were also found to disappear in mice in which Zeb2 loss was induced by tamoxifen administration  (Figures S7D and S7E). The brain and colon were unfortunately not assessed but the conserved effects in the liver, lung, and spleen coupled with the reduced population of microglia and increased turnover of CMs strongly suggests that loss of Zeb2 induces mac disappearance across tissues. To further confirm that the loss of ZEB2 results in tissue-specific changes, we examined the overlap between the DE genes in the 5 tissues (Tables  S1, S2 , S4, S5 and S6) and generated a single t-SNE file containing all Zeb2 +/+ and Zeb2 À/À macs from the 5 organs (Figure  7D ).
All experiments were performed in accordance with the ethical committee of the Faculty of Science of the VIB. Bulk RNA Sequencing 25,000 KCs or AMs were FACS-purified into 500ml of RLT plus buffer (QIAGEN) and b-mercaptoethanol. RNA was isolated using a RNeasy Plus micro kit (QIAGEN) and sent to the VIB Nucleomics facility, where the RNA sequencing was performed using a NextSeq sequencer (Illumina). The pre-processing of the RNA sequencing data was done by Trimmomatic. The adapters were cut and reads were trimmed when the quality dropped below 20. Reads with a length <35 were discarded. All samples passed quality control based on the results of FastQC. Reads were mapped to the mouse reference genome via Tophat2 and counted via HTSeqCount. Samples were subsequently analyzed using R/Bioconductor, and the limma (voom) procedure was used to normalize the data.
RNA Sequencing Analysis Sequencing libraries were loaded on an Illumina NextSeq500 (KCs, AMs) or HiSeq (Microglia, splenic macs, colonic macs) with sequencing settings following recommendations of 10X Genomics (26/8/0/98 -2. 1pM loading concentration). Sequencing was performed at the VIB Nucleomics Core (VIB, Leuven). The demultiplexing of the raw data was done by the 10x's CellRanger software (version 2. 0. 0 (KCs, AMs) or version 2. 0. 2 (Microglia, splenic macs, colonic macs); cellranger mkfastq which wraps Illumina's bcl2fastq). The reads obtained from the demultiplexing were used as the input for 'cellranger count' (10x's CellRanger software) which align the reads to the mouse reference genome (mm10) using STAR and collapses to unique molecular identifier (UMI) counts. The result is a large digital expression matrix with cell barcodes as rows and gene identities as columns.
The aggregation of the CREand CRE+ samples was done using 'cellranger aggr' (10x's CellRanger software). For heatmaps, each column corresponds to a single cell. Preprocessing Data Preprocessing of the data was done by the scran and scater R package according to workflow proposed by the Marioni lab  (Lun et al. , 2016). Outlier cells were identified based on 3 metrics (library size, number of expressed genes and mitochondrial proportion) and cells were tagged as outliers when they were 3 median absolute deviation (MADs) away from the median value of each metric across all cells. Low-abundance genes were removed using the 'calcAverage' function and the proposed workflow. The raw counts were normalised and log2 transformed by first calculating ''size factors'' that represent the extent to which counts should be scaled in each library.
Detecting highly variable genes, finding clusters and creating tSNE plots was done using the Seurat pipeline. Marker genes per identified subpopulation were found using the findMarker function of the Seurat pipeline. Additional low quality (low UMI counts, high % of mitochondrial genes), contaminating (potential doublets) and actively proliferating cells were also removed from the analysis (Figure  S1G ). Gene-Expression Analysis by Real-Time RT-PCR RNA was purified from 10000-25000 sorted cells using an RNeasy Plus micro kit (QIAGEN). RNA was reverse transcribed to cDNA with an iScript Advanced cDNA Synthesis kit (Bio-Rad Laboratories). Gene expression was assayed by real-time RT-PCR using a SensiFast SYBR NoRox kit (GC Biotech) on a PCR amplification and detection instrument (LightCycler 480; Roche) with the primers listed in the Key Resource Table.
ACKNOWLEDGMENTS We thank  K. Vergote ,  G. Blanke , and  J. Taminau  for genotyping, the  Flow Cytometry Core Facility  for assistance, and  A. Mowat  for critical reading of the manuscript. This study benefitted from data assembled by the  Immgen Consortium. We would also like to thank  VIB TechWatch  and the  Single Cell Accelerator program  for supporting the 10x genomics platform. We thank  F. Fiore ,  A. Adda-Benatia , and  F. Danjan  for construction of the Fcgr1-IRES-iCre-TEAL mice and  A. Baranska  for her initial characterization of the Fcgr1-cre line (data not shown). C. L. S. is supported by a  Marie Curie IEF  and a  Sir Henry Wellcome Postdoctoral Fellowship. B. M. is supported by  PHENOMIN  and  ERC  (FP7/2007-2013 grant n  322465 ), P. V. is supported by  FWO ,  EOS  ( 30826052   MODEL-IDI ), and  Methusalem  ( BOF16/MET_V/007 ), G.
INTRODUCTION Logical modeling has been widely used to study gene regulatory and signalling networks (see e. g. ,  Glass and Siegelmann, 2010; Saadatpour and Albert, 2012; Abou-Jaoudé et al. , 2016). Briefly, in a logical model, the evolution of the discretised level of each component depends on the current values of its regulators whose influences are dictated by logical functions. Here, we rely on the generalized framework initially introduced by  Thomas and d'Ari (1990)  and implemented in our software tool GINSIM  (Chaouiya et al. , 2012; Naldi et al. , 2018). Because precise knowledge of the durations of underlying mechanisms is often lacking, one assumes that, when multiple components are called to change their levels, all update orders have to be considered. This corresponds to the asynchronous updating scheme  (Thomas and d'Ari, 1990; Thomas, 1991).
IMPLEMENTATION Both FIREFRONT and AVATAR are implemented in the context of GINSIM, which supports the definition and analysis of logical models  (Chaouiya et al. , 2012; Naldi et al. , 2018). Figure  3  provides a snapshot of the desktop GUI, showing the selection of the algorithm, specification of model modifications (perturbation or reduction), initial conditions, and algorithm parameters. MONTECARLO simulations are also available, as well as a modified version of AVATAR with the approximate strategy described above. User documentation of Firefront and Avatar is provided in the Supplementary Material S2. The implementations of FIREFRONT and AVATAR rely on adequate data structures-states are easily indexable through meaningful and compact hash keys, and sets of states are implemented as a map of states for highly efficient indexations, additions and removals.
RESULTS To validate the proposed algorithms, we considered a number of case studies including randomly generated, synthetic and published biological models. All are briefly described below. We analyzed how FIREFRONT and AVATAR perform on these case studies and compared, when possible, to outcomes produced by BOOLNET  (Müssel et al. , 2010)  and MONTECARLO simulations. BOOLNET is an R package not only able to generate random Boolean models, but also to identify attractors and to perform Markov chain simulations. We further compared AVATAR with MABOSS, a C++ software implementing a Monte Carlo kinetic algorithm to produce time trajectories of Boolean models  (Stoll et al. , 2012) , and with the probabilistic model checker PRISM  (Kwiatkowska et al. , 2011 (Kwiatkowska et al. , , 2017)). The experiments were run using an Intel(R) i7-7500U CPU @ 2.
Prp19 complex and splicing machinery prevent the nuclear accumulation of GLP-1 Prp19 is central to a large protein complex, known as the Prp19 complex or NineTeen Complex (Prp19C/NTC). Prp19C/NTC consist of eight core proteins and up to 19 associated proteins in yeast, and more than 30 proteins in higher eukaryotes, including in animals and plants [for review see  (Chanarat and Sträßer, 2013)  and  (Ambrósio et al. , 2015) ; Table  S2 ]. Although this complex remains uncharacterized in C. elegans, we RNAi-depleted several putative worm components of the Prp19C/NTC to test whether the PRP-19 effect on nuclear GLP-1::GFP may involve Prp19C/NTC. We found that the depletion of several putative Prp19C/NTC components promoted the nuclear accumulation of GLP-1::GFP, with varying penetrance (Fig. 5 ); at the same time, their depletion did not alter the overall abundance or nuclear localization of PRP-19::STREP (data not shown).