dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This research was supported by the  Fonds Québécois de Recherche Société et Culture -Programme de soutien aux équipes  (demande no. 179537 ). Jérôme Servonnat  is thanked for his input on the IPSL-CM5A-LR model biases. The LSCE team thanks  Armel Martin  for his patient and careful work on the downscaling method, quite some years ago. We thank  Stéphane Raynaud  for his vacumm package, in particular for the regridding tools used in the present work. Edited by: Jeremy Fyke Reviewed by: two anonymous referees.
2014, 2017). Code and data availability. The code used for the downscaling and the input and output data are available at https: //figshare. com/s/1b952e47ff274cc0687e (last access: 25 June 2018, https://doi. org/10. 6084/m9. figshare. 5487145, Latombe et al. , 2018). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-11-2563-2018-supplement.
Acknowledgment The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory ("Argonne"). Argonne, a U. S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U. S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. http://energy. gov/downloads/doe-public-access-plan  (Saturation fluence for a carbon atom at 290 eV, just above the K-edge, is ∼10 10 photons/microns 2.
The stability should improve with repetition rate. If timing, spectrum and pulse energy can be controlled better, single-shot data collection may be unnecessary for many experiments. Many of the challenges identified above could be resolved in next-generation machines. For example, several methods are under development to produce sub-femtosecond x-ray pulses. The XLEAP project at SLAC is based on laser-manipulation of the electron bunch phase space to produce isolated sub-femtosecond x-rays precisely timed to an external laser. XLEAP will begin commissioning this year and, if successful, might become a standard operation at LCLS-II  [22]. In addition, coincidence techniques will be useful over an enlarged spectral range at the European XFEL and LCLS-II. Emission and RIXS experiments will benefit from the flux increase due to the higher repetition rates, easing constraints on the sample concentration and systematic studies.
Concluding remarks. The new field of femtosecond x-ray probes of molecules is beginning to have an impact in molecular physics, chemistry, and biology. New sources that will be available to the research community in the next decade will expand access to these unique probes and allow new transformative methods. Acknowledgments. MG is funded by the Volkswagen foundation via a Lichtenberg professorship. PHB is supported through the Stanford PULSE Institute, SLAC National Accelerator Laboratory by the US Department of Energy, Office of Basic Energy Sciences, Atomic, Molecular, and Optical Science Program.
The main drawback of this approach is that it cannot account for effects related to dynamical changes of the chemical-bond network. XMDYN is part of the start-to-end simulation package for biomolecular imaging at the European XFEL  [114]. When it comes to the development of short-wavelength nonlinear spectroscopy  [75, 115] , the challenges for electronic-structure theory are maybe even more severe. Spectroscopy fundamentally relies on the availability of quantitatively accurate theory. Theoretical single-photon x-ray spectroscopy is already rather difficult; it is obvious that the nonlinear regime has additional challenges in store. It must also be realized that nonlinear spectroscopy in ultrafast AMO physics is not limited to photon(s)-in photon(s)-out schemes; electron detection is equally important.
Acknowledgment. The support of the  Chemical Sciences, Geosciences , and  Biosciences division, Office of Basic Energy Sciences, Office of Science, US Department of Energy  through Award No. DE-FG02-04ER15571  and the  National Science Foundation  award  CHE-1663822  is gratefully acknowledged. 3. 2. Nonlinear x-ray pump-probe spectroscopy with  XFELs Nina Rohringer CFEL/DESY  and  University of Hamburg Acknowledgments. Many stimulating discussions with colleagues in various collaborations [69, 75, 77, 79, 80], especially at the European XFEL and within the Collaborative Research Center SFB 925 are acknowledged. Acknowledgments. I would like to acknowledge fruitful discussions with  Tais Gorkhover ,  Phay Ho  and  Linda Young. This work was supported by the  US Department of Energy, Office of Science, Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division.
Acknowledgments. Work by MF is supported by the  US Department of Energy, Office of Science, Basic Energy Sciences  under Award # DE-SC0016494. Work by DR is supported by the  AMOS program within the Chemical Sciences, Geosciences, and Biosciences Division of Basic Energy Sciences, US Department of Energy  under Award # DE-AC02-76SF00515. Acknowledgments. The thoughts presented in this contribution were shaped in part by discussions within the Collaborative Research Center SFB 925 and by my collaboration with  Nora Berrah ,  Henry Chapman ,  Yi-Jen Chen ,  Gopal Dixit ,  Daria Gorelova ,  Kota Hanasaki ,  Yajiang Hao ,  Ludger Inhester ,  Zoltan Jurek ,  Antonia Karamatskou ,  Zheng Li ,  Adrian Mancuso ,  Michael Meyer ,  Daniel Rolles ,  Artem Rudenko ,  Sang-Kil Son ,  Koudai Toyota ,  Kiyoshi Ueda ,  Oriol Vendrell ,  Linda Young  and  Beata Ziaja.
Acknowledgments. The work described herein was demonstrated over a period >10 years. We gratefully acknowledge funding by the  National Science Foundation  ( ERC ENG-0310717 ;  PHY-1125844 ;  STC DMR-1548924 ), the  Department of Energy (BES AMOS) , the  DARPA   PULSE program  ( W31P4Q-13-1-0015 ), and through the  Gordon and Betty Moore Foundation's EPiQS Initiative  through Grant  GBMF. 5. 2. Back  to the future-long wavelength lasers  Heide Ibrahim  and  François Légaré Institut National de la Recherche Scientifique ,  Centre Énergie ,  Matériaux , et Télécommunications Acknowledgments. The author gratefully acknowledges support from the  National Science Foundation , the  Department of Energy , the  Army Research Office , the  Air Force Office of Scientific Research , and the  Defense Advanced Research Projects Agency , as well as important input from group members  Peter Kraus ,  Andrew Attar ,  Erika Warrick , and  Michael Zuerch.
Atlas of Living Australia (ALA) (https://www. ala. org. au/) is the Global Biodiversity Information Facility (GBIF) node of Australia. They developed an open and free platform for sharing and exploring biodiversity data. All the modules are publicly available for reuse and customization on their GitHub account (https://github. com/AtlasOfLivingAustralia). GBIF Benin, hosted at the University of Abomey-Calavi, has published more than 338 000 occurrence records from 87 datasets and 2 checklists. Through the GBIF Capacity Enhancement Support Programme (https://www. gbif. org/programme/82219/capacityenhancement-support-programme), GBIF Benin, with the help of GBIF France, is in the process of deploying the Beninese data portal using the GBIF France back-end architecture. GBIF Benin is the first African country to implement this module of the ALA infrastructure.
Microarray analysis Three independent total RNA isolations were performed on Mef>bru-3(37) and Mef>lacZ third-instar larvae using TRIzol reagent (Invitrogen) and the Agilent Drosophila gene expression microarray (G2519F, Strasbourg, France). Treeview (version 1. 60, University of California at Berkeley) was used to confirm similarity (>70% Pearson correlation) between triplicates. The cutoff was set to a P-value <0. 001 and a twofold increase/decrease. Differentially expressed genes were then classified according to the biological process they are involved in (Flybase). Microarray data are available in the ArrayExpress database under accession numbers E-MTAB-3231 and E-MTAB-1469.
In silico positioning of destabilizing motifs in 3′UTR sequences 3′UTR sequences were collected from UCSC (http://genome. ucsc. edu/) databases and BLASTed with either destabilizing motifs  (Lee et al. , 2010)  (Table  S3 ) or random motifs (Table  S4 ) using a script purpose-developed in C++. Enrichments in the group of transcripts bound by CELF1 versus unbound transcripts group were tested for significance using Khi2 test without Yates' correction. BLAST positions for each sequence were sorted to generate hand-made diagrams in Excel.
Statistics All statistical analyses were performed using GraphPad Prism (version 5. 02) software. Normality of the samples was assessed with a Kolmogorov-Smirnov test. One-way ANOVA (Newman-Keuls multiple comparison test or Dunn's multiple comparison post-test) were used for statistical comparisons of each pathologic line against its respective driver control line and/or transgenic control line. The smallest significance of both comparisons is reported on the graph. A Mann-Whitney test was only performed to compare the DM1 960 line with the DM1 960 ,Df(bru-3) in rescue experiments, or the two bru-3 overexpressing lines where reported. Results are reported as means±s. e. m. , with P<0. 05 considered statistically significant.
The first structures of recombinant mammalian membrane proteins were solved in 2005 using protein that had been produced in yeast cells: the rabbit Ca 2+ -ATPase, SERCA1a, was produced in Saccharomyces cerevisiae  [5]  and the rat voltage-dependent potassium ion channel, Kv1. 2 was produced in Pichia pastoris  [6]. These early results established microbes as efficient and effective host systems for synthesizing membrane proteins. While baculovirus-infected insect cells and mammalian cell-lines have been used very successfully for both prokaryotic and eukaryotic membrane protein production  [1] , we note that microbes have remained a consistently-popular choice because they are quick, easy and cheap to culture and they can produce high-quality protein suitable for subsequent study. In November 2017, Stephen White's database (blanco.
biomol. uci. edu/mpstruc/) recorded that almost a third (31%) of all membrane protein coordinate files deposited in the Protein Data Bank (www. rcsb. org/pdb/home/home. do; PDB) were derived from recombinant proteins; notably, 71% of all unique structures were derived from microbial sources. Of these, 64% were produced in E. coli, 4% in P. pastoris and 3% in S. cerevisiae. This review focuses on current approaches to selecting expression plasmids (especially with respect to their purification tags and promoters), microbial strains and culture conditions to enable the detergent-based purification of functional membrane proteins for biophysical characterization and crystallization trials (for subsequent.
An overview of microbial host usage The expression systems used in generating high-resolution structures of recombinant membrane proteins have been documented by Stephen White in his analysis of the PDB (blanco. biomol. uci. edu/ mpstruc/). Biophysical studies of membrane proteins (especially NMR and crystallographic techniques) require large quantities (0. 1-10 mM) of homogenous, correctly-folded, purified protein; a focus on data extracted from the PDB has therefore allowed us to identify systems that have the capability of producing the required quantity and quality of these challenging targets. This review updates our previous study  [8]  of E. coli expression systems and extends that work to include S. cerevisiae and P. pastoris. Together, these three host systems account for the production of the vast majority of recombinant membrane proteins in microbes, although Lactococcus lactis (see PDB entry 4US3), Pseudomonas fluorescens (5KUD) and Schizosaccharomyces pombe (2PNO) have also been used successfully as microbial cell factories in a minority of cases.
coli (Table  6 ), we identified 47 uMPS. Of these 47 uMPS, 7 were monotopic membrane proteins and 23 were small peptides or proteins containing only one transmembrane domain. Fifteen uMPS were produced as inclusion bodies and subsequently refolded, 17 were purified in mild detergent and, of those, 3 were membrane proteins with more than 4 transmembrane α-helices that had been crystallized in the presence of detergent (PDB codes: 2Q7M; 4BUO; 4O6Y) and 1 was studied by electron microscopy (3DWW).
Selection of yeast expression strains for improved membrane protein production A comprehensive strain collection exists from which potential expression hosts can be selected, supported by information in the Saccharomyces Genome Database (http://www. yeastgenome. org/). The yeast deletion collections comprise over 21,000 mutant strains with precise start-to-stop deletions of approximately 6000 S. cerevisiae ORFs  [74]. The collections include heterozygous and homozygous diploids as well as haploids of both MATa and MATα mating types. Individual strains or the complete collection can be obtained from Euroscarf (http://web. uni-frankfurt. de/fb15/mikro/euroscarf/) or the American Type Culture Collection (http://www. atcc. org/). Dharmacon sells the Yeast Tet-Promoters Hughes Collection (yTHC) with 800 essential yeast genes under control of a tetracycline-regulated promoter that permits experimental regulation of essential genes.
Acknowledgements We acknowledge funding from the  Biotechnology and Biological Sciences Research Council (BBSRC ; via grants  BB/N007417/1 ,  BB/ P025927/1  and  BB/P022685/1  to RMB) and the  Innovative Medicines Joint Undertaking  under Grant Agreement number  115583  to the  ND4BB ENABLE Consortium  to RMB. Parts of the research for this review were funded through the  EC Membrane Protein Consortium (E-MeP ,  FP6 LSHG-CT-2004-504601 ), the  European Drug Initiative for Channels and Transporters  (EDICT,  FP7 Health-F4-2007-201924  and  BBBSRC  grant  BB/D524832/1  to RMB and PJFH. MVD's PhD studentship was funded by  BBSRC  iCASE  BB/H016244/1  with Glycoform Ltd. PJFH is grateful to the  Leverhulme Trust  for an  Emeritus Research Fellowship  (Grant number  EM-2014-045 ). Fermenters in PJFH's laboratory and allied equipment were funded by the  BBSRC  ( MPSI BBS/ B/14418 ), the  Wellcome Trust  ( JIF 062164/Z/00/Z ) and the  University of Leeds.
We acknowledge the ' Initiative d'Excellence' program  from the  French State  (Grant ' DYNAMO ',  ANR-11-LABEX-0011-01 ). MP's PhD fellowship is funded by the  French Ministry of Research and Education. Fig. 12. Detergent usage for the solubilization and crystallization of membrane proteins. Data were obtained from Tables 1-4. Detergent usage for the solubilization (grey) and crystallization (black) of (A) β-barrel membrane proteins produced in E. coli and of α-helical membrane proteins produced in (B) E. coli, (C) S. cerevisiae or (D) P. pastoris. Detergents are classified as follows: Maltosides (DDM, UDM, Tri-DM, DM, NM, OM and TDM); Glucosides (OG, OTG, NG,  C7G ); Polyoxyethyleneglycols ( C12E9 ,  C12E8 ,  C10E5 ,  C8E4 ,  OPOE  and  C12E7 ); Fos cholines ( FC12  and  FC14 ); Cymals (Cymal 4, Cymal 5 and Cymal 6); Neopentylglycols (LMNG, DMNG and OGNG); Amine oxides (LDAO and DDAO); Zwittergent ( SB-3 14  and  SB-3 12 ); Mixed (Mixed detergents).
Optimized Conditions Do Not Eliminate Morpholino Side Effects Our re-analysis of public RNA-seq datasets  (Campbell et al. , 2016; Chung et al. , 2014; Dichmann et al. , 2015; Marletaz et al. , 2015; Noiret et al. , 2016) -with the caveat that KO references were not available for these-reinforced that the immune response and mis-splicing were likely common side effects of MO-mediated KDs in Xenopus embryos (Figure  6 ). The strength of immune response as inferred from the transcriptional induction of c3ar1, tp53inp1, and tp53 increased with developmental progression as well as the GC content and the amount of the injected MO (Figure  6A ). The comparison of mis-splicing defects induced by the cMO between early neurula  (Marletaz et al. , 2015)  and tailbud (this study) embryos confirmed the strong correlation with cMO-complementary sequence at blocked splice junctions (e.
The reads of public RNA-Seq datasets (study accession # PRJNA351216,  Campbell et al. , 2016; PRJNA218018, Chung et al. , 2014; PRJNA266550, Dichmann et al. , 2015; PRJNA290093, Marletaz et al. , 2015; PRJEB8711, Noiret et al. , 2016)  were aligned to the genome assemblies of X. tropicalis (v7. 1) or X. laevis (v9. 1) using the STAR aligner v2. 5. 3a with default settings. Differential expression analysis was performed with raw fragment counts excluding those belonging to ribosomal and mitochondrial RNA using DESeq2 v1. 14. 1  (Love et al. , 2014). Except for the principal component analysis (PCA) gene-specific dispersion estimates were calculated separately for the t/t2 KO and KD experiment. In an effort to find genes with consistent fold changes over time, p-values were generated according to a likelihood ratio (c 2 ) test reflecting the probability of rejecting the reduced ($ developmental stage) over the full ($ developmental stage + condition) model.
Resulting p-values were adjusted to obtain false discovery rates (FDR) according to the Benjamini-Hochburg procedure, whereby thresholds on Cook's distances and independent filtering were switched off (Table  S2 ). For PCA normalized fragment counts were transformed with a regularized logarithm (rlog) to shrink substantial variance among low-count genes (Figure  2A ). For all other analyses, genes with %7 fragment counts averaged between cMO-injected and uninjected embryos and wild-type and t/t2 heterozygotes were removed (Figure  S2B ). This lower threshold was set because markedly more falsely discovered fold changes were detected below it between otherwise very similar conditions. Public datasets were also analyzed using DESeq2, but statistical significance of transcript level differences between morphant and corresponding uninjected embryos were obtained through Wald tests (Figure  6A ).
Perturbation Networks of Biological Processes Common and unique changes (R1. 5-fold change at FDR %10%) of gene expression among four comparisons (control or t/t2 morphants versus uninjected embryos and t/t2 heterozygotes or homozygotes versus wild-type embryos) were calculated and visualized in two Venn diagrams (Figure  2C ) for elevated and reduced transcript levels using limma v3. 30. 13  (Ritchie et al. , 2015). The five largest Venn fields were analyzed for enriched biological processes (BP) using GOstats v2. 40. 0  (Falcon and Gentleman, 2007)  and GSEABase  (Morgan et al. , 2017). Gene-specific BP term associations were previously generated using BLAST2GO  (Conesa et al. , 2005; Gentsch et al. , 2015; Owens et al. , 2016). Parental, regulatory and component relationships between enriched BP were visualized as 'perturbation networks' using igraph v1.
For visualizing tp53 isoforms (Figure  3D ) paired-end reads were mapped to the X. tropicalis genome assembly v7. 1 and known off-genome EST assemblies using Tophat v2. 0. 10 (  Kim et al. , 2013)  with the following parameters: -r 77 (mean inner distance between mate pairs) -mate-std-dev 110 (standard deviation of inner distances between mate pairs) -G v7. 2 (gene models of version 7. 2 as used above) -g 200 (up to 200 alignments per read) -report-secondary-alignments (include additional and secondary alignments). Tophat BAM files of biological replicates were merged using samtools v1. 3. 1  (Li et al. , 2009)  and converted to the bigWig format. These genome tracks were normalized to the wigsum of 1 billion excluding any reads with mapping quality <10 using the python script bam2wig. py from RSeQC v2.
Analysis of Differential Splicing Splicing anomalies were detected without transcript annotation applying LeafCutter v1. 0  (Li et al. , 2018)  on STAR-aligned split reads from all conditions. LeafCutter focuses on intron splicing events rather than whole transcript isoform quantification which helped to reduce false positive accounts caused by whole transcript fold changes. The two-pass mode and otherwise default settings of STAR v2. 5. 2/3a were used to align split reads to the genome assemblies of X. tropicalis (v7. 1) or X. laevis (v9. 1)  (Dobin et al. , 2013). The scripts 'leafcutter_cluster. py' and 'utils. R' were modified accordingly to accept scaffold coordinates and to record all junctions that contain R1 read. LeafCutter clusters introns according to shared acceptor or donor sites.
Seq module in Julia (https://github. com/BioJulia). The longest consecutive number of matching bases was calculated at each position of either strand of the genome, and at each position of the reverse strand of the transcriptome, recording positions with R8 consecutive matches and within 75 bp from putatively blocked splice sites. The heatmap values were reordered according to the differential use (log 2 fold changes) of introns with a minimal PSI per cluster using Anti-Robinson seriation by simulated annealing (ARSA)  (Hahsler et al. , 2008). The Mann Whitney U test was applied to find out whether MO sequence alignments (R10 consecutive base matches) were enriched at mis-spliced junctions compared to all gene annotated splice junctions (R20 split reads detected among all samples) and whether negative transcript level changes were greater at mis-spliced genes than all other genes with confirmed introns (R20 split reads detected among all samples).
ACKNOWLEDGMENTS We thank  Lyle Zimmerman  and  Tosikazu Amano  for helping with the X. tropicalis frogs, and the  Aquatics and Advanced Sequencing Facility  for frog maintenance and sequencing;  Mareike Thompson ,  Andreia Bernardo , and  Victor Tybulewicz  for critical reading of the manuscript; and the  Smith lab  for discussions and advice. G. E. G. and J. C. S. are supported by the  Francis Crick Institute , which receives its core funding from  Cancer Research UK  ( FC001-157 ), the  UK Medical Research Council  ( FC001-157 ), and the  Wellcome Trust  ( FC001-157 ). R. S. M. is supported by  DevCom , a  European Initial Training Network for Developmental and Computational Biology  (Project Reference 607142).
DATA AND SOFTWARE AVAILABILITY Raw RNA-Seq data (FASTQ files) and annotation files reported in this paper are available in the GEO database (www. ncbi. nlm. nih. gov/geo) under the accession number GEO: GSE96655. All analyses were performed in R v3. 3. 1 / Bioconductor v3. 5, Perl v5. 18. 2, Python v2. 7. 12 or Julia v0. 5 as described in the detailed methods. e10 Developmental Cell 44, 597-610. e1-e10, March 12, 2018.
Sequence Data Analysis and Taxonomic Identification The 16S rDNA variable region 4 (V4) was sequenced in Illumina MiSeq 2000 Platform. Sequences are available in NCBI Repository (BioProject PRJNA482612, accession: http://www. ncbi. nlm. nih. gov/bioproject/482612). Sequence processing to get an Operational Taxonomic Unit (OTU) table was performed following QIIME software v1. 9. 1 (Quantitative Insights In Microbial Ecology;  [44] ) and recommendations on genomic data processing  [45] [46] [47]. Briefly, sequences of paired-read amplicon libraries were paired-end aligned using fastq-join method  [48] , with a minimum overlap of 100 base-pairs and maximum 10% difference in the overlapping region. Then demultiplexion and quality filtering (at Phred ≥ Q20) was performed, and sequences trimmed to 400 base-pairs with Usearch  [49].