dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, this work would not have been possible without the support (in part) of the  Deutsche Forschungsgemeinschaft (DFG) , the  Agence Nationale de la Recherche  (Project  ID :  ANR-16-CE92-0009  ' ImmunoglobulinCrowding '), the  ILL  and the  Studienstiftung des Deutschen Volkes. Academy of Sciences (USA)   106 ,  20758-20763. Hwang J, Kim J and Sung BJ  ( 2016 ) Dynamics of highly polydisperse colloidal suspensions as a model system for bacterial cytoplasm. Physical Review E 94, 022614. Ibrahim Z,  Martel A ,  Moulin M, Kim HS ,  Härtlein M, Franzetti  B and Gabel F (2017) Time-resolved neutron scattering provides new insight into protein substrate processing by a AAA+ unfoldase. Scientific Reports 7, 40948. Igumenova TI ,  Frederick KK  and  Wand AJ  ( 2006 ) Characterization of the fast dynamics of protein amino acid side chains using NMR relaxation in solution.
5 and stored at -20 °C. SDS-PAGE analysis was performed according to the method of Schägger and von Jagow 50. Biophysical analysis. OBP19b was analyzed by MALDI-ToF mass spectrometry with a Voyager DE-Pro (Sciex) spectrometer using a positive linear mode. Circular dichroism spectra were recorded using JASCO spectrometer J-815 equipped with a Peltier temperature control system (JASCO MPTC-490S) and a 0. 1 mm thick quartz cell. Measurements were taken at a protein concentration of 5. 0 mg/mL in 50 mM sodium phosphate at pH 7. 5. Ten measurements were recorded for each spectrum over a range of 180 to 260 nm at 20 °C with a data pitch of 0. 5 nm and a scanning speed of 100 nm/min. The contribution of the buffer was corrected for and converted into molar ellipticity. Fluorescence-based-binding assay.
Competitive ligand-binding experiments were performed using N-phenyl-1-naphthylamine (NPN) as a fluorescent probe  51. Emission fluorescence spectra were recorded using a Cary Eclipse spectrofluorometer (Agilent Technologies) with a 1 cm light path, quartz cuvette and 5 nm slits for both excitation and emission. To determine the binding affinity of OBP19b for NPN, a 2 µM protein sample in 50 mM sodium phosphate buffer at pH 7. 5 was titrated with aliquots of 10 mM NPN in 10% methanol to a final concentration of 0. 5-10 µM. The excitation wavelength of NPN was 337 nm, and the fluorescence emission was recorded between 380 and 450 nm. The value of the dissociation constant of the complex OBP19b/NPN (K NPN ) was calculated using SigmaPlot software via nonlinear regression of a unique binding site.
Test solutions were diluted in 50 mM sodium phosphate buffer at pH 7. 5 and colored with the red dye sulforhodamine B (3520-42-1; Sigma-Aldrich) to easily visualize the surface boundary of the test solution. Mineral oil was overlaid on the minicaps to prevent evaporation. The feeding test lasted 6 h under white light at room temperature (25 °C) and 50-60% humidity. Simultaneously, for each series of tests, we included a similar CAFE device without flies to determine the evaporation level, which was subsequently subtracted from the experimental levels measured. All experiments were performed between 8 a. m. and 2 pm. Images of capillaries were captured before and after the test, and the amount of food intake was measured using ImageJ software. Amino acid consumption was evaluated with a preference index (PI) according to the following equation: PI = (V aa -V c )/ (V aa + V c ), where V aa and V c represent the volume of the amino acid-rich and the control solution consumed, respectively.
In toto, labella were transferred through blocking buffer (0. 5% blocking reagent, 0. 15 M NaCl, and 0. 1 M Tris HCl at pH 7. 5) for 45 min under agitation at RT. The whole-mount fly labella was first incubated in goat anti-GFP (1:500, Rockland) and rabbit anti-mCherry (1:1000, Rockland) primary antibodies for 48 hours at 4 °C and washed 5 times for 15 min with 1x PBS and 0. 2% Triton. Thereafter, tissues were incubated overnight in anti-goat-Alexa 488 (1:400 antibody, Molecular probes) and anti-rabbit-Alexa 594 (1:400 antibody, Molecular probes) secondary antibodies in the dark at 4 °C. Tissues were then washed with 1x PBS and 0. 2% Triton five times for 15 min and were mounted with Vectashield H-1200 in DAPI medium. The labeling was visualized with a Leica sp8 confocal microscope and analyzed using ImageJ software (Software, NIH, Bethesda, MD, USA).
Statistics and reproducibility. The competitive-binding assay data were compared to the buffer using one-way ANOVA followed by Dunnett's test (***p < 0. 001; **p < 0. 01), the experiments were reproducible, N = 4-13. The evaluation of statistical significance of differences of the CAFE assay data was performed with Kruskal-Wallis test to compare same-sex treatments and post hoc Wilcoxon test to assess two-by-two differences between genotypes and tested amino acids. The experiments were reproducible and for each Drosophila strain the experiments were repeated 7 to 15 times (each tube containing ten flies). The electrophysiological data were analyzed by comparing the number of spikes/s between genotypes and tested amino acids using ANOVA and Tukey's post hoc test (p < 0. 001). The experiments were reproducible and for each Drosophila strain the experiments were repeated eight to 12 times.
Acknowledgements We thank  Bloomington Drosophila Stock Center ,  FlyORF Zurich ORFeome Project ,  Seok Jun Moon  and  Craig Montell  for D. melanogaster stocks,  Claude Everaerts  for help with the statistics and DIMACell platform for microscopy studies ( University of Burgundy, Dijon, France ). Mass spectrometry experiments were performed by the  Plateforme SFR BioSciences  ( UMS 3444 ),  Protein Science Facility (PSF)  of the  Institut de Biologie et Chimie des Protéines (IBPC CNRS/Université de Lyon , 7 Passage du Vercors  69367 LYON Cedex 07 ).
Kinetic analysis of the catalytic activity of GDH in the virus-scaffolded integrated system. Cyclic voltammograms were recorded at a gold electrode bearing random arrays of fd-particles immunodecorated by the Fc-PEG / PQQ-GDH system for increasing glucose concentrations. As seen in Figure  6A , it was observed that the plateau current of the catalytic signal increased in intensity with increasing glucose concentration, C°g, up to 30 mM, but then decreased when C°g was further increased. i cat,pl as a function of glucose concentration C 0 g for two surface coverages in decorated fd particles, fd. Plain lines are the theoretical fits of Equation 2 to the experimental data using the here-determined kinetic constants reported in Table  1. Scan rate (v = 0. 01 V/s). This behavior is indicative of the well-documented substrate inhibition of PQQ-GDH.
Acknowledgments. Dr. Nicolas Mano  is warmly thanked for providing the pgp492 plasmid used to produce PQQ-GDH. We thank  Drs. Benoit Limoges ,  François Mavré  and  Mathieu Branca  for the generous gift of a PQQ sample. Vincent Guérineau, www. imagif. cnrs. fr, is thanked for  Maldi-Tof  mass spectrometry analysis. This work has received financial support from the  French "Agence Nationale de la Recherche" (ANR)  through  "eVIRZYM"  ( DS0401 -ANR 2014-CE09-0009 ).
KG, Düren, Germany) according to the manufacturer's protocol and cloned in TOP10 chemically competent Escherichia coli cells using the TOPO TA Cloning R Kit for Sequencing (Invitrogen, Carlsbad, CA, USA). Clones were selected for sequencing after confirmation of the correct insert size by M13 colony-PCR and visualization on agarose gel electrophoresis. Plasmid extractions and sequencing of cloned sequences were processed by LGC Genomics (Berlin, Germany). Gene sequences have been deposited in GenBank under accession numbers MK978748-MK978767.
16S rRNA Gene Amplicon Sequencing and Analysis PCR amplicons of 16S rRNA genes were obtained using primers 519F (5 ′ -CAGCMGCCGCGGTAA-3 ′ )  (Burggraf et al. , 1997)  and 805R (5 ′ -GACTACNVGGGTWTCTAAT-3 ′ )  (Apprill et al. , 2015) , which amplify both archaeal and bacterial genes. PCR amplicons were sequenced with Illumina MiSeq Paired-End sequencing (2x300 bp) at the Vienna BioCenter Core Facilities, Vienna. Data analysis was performed with QIIME2  (Caporaso et al. , 2010). Adaptor sequences were first trimmed from reads using CUTADAPT  (Martin, 2011)  and then processed using the DADA2 pipeline  (Callahan et al. , 2016). Amplicon Sequence Variants (ASVs) were classified using VSEARCH  (Rognes et al. , 2016)  with a custom-modified Silva v132 database.
20. 4 (minimum length: 90, minimum quality: 28)  (Schmieder and Edwards, 2011, RRID:SCR_005454). Filtering of IonTorrent reads (minimum length: 80, maximum length: 400 bp, minimum quality: 20) was performed using cutadapt 1. 12  (Martin, 2011, RRID:SCR_011841). After filtering, we obtained 40M read pairs from the Illumina dataset and 4. 6M reads from the IonTorrent dataset. All filtered reads were assembled using SPAdes 3. 7 using kmer sizes from 11 to 105 by steps of 2  (Bankevich et al. , 2012; RRID:SCR_000131). The filtered reads from both Illumina and IonTorrent were separately mapped onto the metagenomic assembly using NextGenMap 0. 5. 2  (Sedlazeck et al. , 2013, RRID:SCR_005488). The read coverage of the SPAdes assembly was calculated with SAMtools 1. 3. 1  (Li et al. , 2009 , RRID:SCR_002105) for both IonTorrent and Illumina datasets.
We then used the differential coverage binning approach  (Albertsen et al. , 2013)  by plotting the coverages obtained using both sets of reads against each other, and using the % GC (Figure  S5 ). Twenty-two scaffolds (27 contigs) above 5 kb were selected as belonging to the genome of Ca. N. arcticus Kfb. Five additional metagenomic bins were assigned to five distinct bacterial contaminants (Table  S1 ). The genome sequence of Ca. N. arcticus Kfb has been deposited in NCBI under BioProject PRJNA505990 and BioSample SAMN10440610.
10. 13  (Huson et al. , 2016)  with LCA parameter top 5% and accession-to-taxonomy mapping file prot_acc2tax-Oct2017X1. abin and GenBank's nonredundant database (NRDB version 16/11/2017; BLASTP 2. 7. 1+ with E-value cutoff e-10). For a summary of the results see Table  S1.
Based on a recent global phylogeny-guided taxonomy of archaeal amoA genes by  Alves et al. (2018)  the candidate genus Nitrosocosmicus represents AOA clade NS-ζ (NS-Zeta) within the order-level lineage NS (i. e. , order Nitrososphaerales) (Figure  4A ), which was previously referred to as "Nitrososphaerasister cluster"  (Pester et al. , 2012). Ca. N arcticus Kfb is specifically associated with one of several basal OTUs within clade NS-ζ, which also includes the two major subclades NSζ-1 (without cultivated organisms), and NS-ζ-2, represented by  ). All branches are highly supported and OTUs defined at 96% sequence identity  (Alves et al. , 2018). (B) Global environmental distribution of clade NS-ζ based on 364 amoA genes in the curated database  (Alves et al. , 2018). Sequences from polar, boreal and alpine soils and from cold springs were further categorized here.
, 86% for N. viennensis EN76, and 92% for N. maritimus SCM1; data from Microscope  (Medigue et al. , 2017) ]. The presence of a full 3-hydroxypropionate/4hydroxybutyrate carbon fixation pathway and all genes for gluconeogenesis indicates that Ca. N. arcticus Kfb is in principle capable of autotrophic growth like other AOA strains  (Könneke et al. , 2014; Kerou et al. , 2016b). The organism can also produce polyhydroxyalcanoates as carbon storage compounds  (Poli et al. , 2011)  and compatible solutes (mannosylglycerate)  (Empadinhas and da Costa, 2008) , like several other AOA. Ca. N. arcticus Kfb also encodes two carbonic anhydrases, enabling it to interconvert between bicarbonate and CO 2  (Ferry, 2010) , as found in several other marine and terrestrial lineages of AOA. The genome encodes six members of the glucose/sorbosone dehydrogenase family proteins (PF07995, arCOG02796), a protein family generally expanded among Ca.
Flow cytometry analysis Analyses of cell phenotype and proliferation rates were performed on splenic, thymic and bone marrow lymphoid populations using a four-color fluorescence cytometer (FACSCalibur Lineage staining correspond to the following antibodies: anti-mouse CD3, CD4, CD8, B220, CD11b, CD11c, Gr1, CD49b and Ter119 (Ly76). These analyses were performed using a tencolor fluorescence cytometer (FACS FORTESSA TM ), according to standard protocols. The following antibodies were used for spleen, bone marrow or thymic proliferation and apoptosis analysis, respectively: FITC anti-BrdU (see 5-bromo-2'-deoxyuridine (BrdU) incorporation) and FITC annexin V (from BD Biosciences). Data were analyzed with FlowJo software (Treestar®).
Quantitative real-time RT-PCR analysis RNA was extracted with RNeasy Kit (Qiagen) and cDNA was obtained with High Capacity Reverse Transcription Kit (Applied Biosystems) using a T100™ Thermal cycler (Biorad). Quantitative real-time PCR was performed on 10 ng of cDNA using Taqman Universal Mastermix (Applied Biosystems) and Assays-on-Demand probes (Applied Biosystems) (Hprt1: Mm01318743_m1, Gapdh: Mm03302249_g1, Sting: Mm01158117_m1, Isg15: Mm01705338_s1, Ifit1: Mm00515153_m1, Cxcl10: Mm00445235_m1, Ifnα4: Mm00833969_s1, Ifnβ1:Mm00439552_s1). Each sample was amplified in triplicate in a StepOnePlus® real-time PCR system (Applied Biosystems). mRNA levels were calculated with the StepOne v2. 1 software (Applied Biosystems), using the comparative cycle threshold method, and normalized to the mean expression of Hprt1 and Gapdh housekeeping genes.
ACKNOWLEDGMENTS We thank  D. Bock ,  M. Duval ,  D. Lamon ,  S. Reibel-Foisset  and  K. Sablon  for excellent animal care. We thank  V. Flacher ,  F. Gros  and  C. Mueller  ( CNRS UPR 3572 "Immunopathology and Therapeutic Chemistry"/Laboratory of Excellence Medalis, Institute of Molecular and Cellular Biology (IBMC), Strasbourg, France ) for scientific discussions. YJC acknowledges the  European Research Council  (GA 309449: Fellowship to YJC). This work was supported by grants from  Strasbourg University (UdS) ,  Centre National de la Recherche Scientifique (CNRS) , by the  Agence Nationale de la Recherche  ( ANR-14-CE14-0026-04 ,  Lumugene ; and  ANR-11-EQPX-022 ) and by  EU-funded (ERDF)  project  INTERREG V "RARENET ". D. B. and F. A. were supported by the  Ministère de la Recherche et de la Technologie.
■ INTRODUCTION Molecular simulations have become increasingly powerful and accessible in recent years, due in part to the rise of HPC  [1] [2] [3]  and GPU-powered clusters and powerful desktop computers  4  as well as the development of user-friendly software to setup simulations. 5, 6 The underlying physical models and methods have also improved over the years to address ever more complex biological and chemical questions. 7, 8 Finally, the number of users and available tools is continuously increasing, as is the amount and complexity of workflows and produced outputs. 9, 10 n this context, defining best practices related to documentation of protocols and code used to generate and/or analyze Molecular Dynamics (MD) simulations is becoming more important than ever. 11 A set of guidelines for reporting results obtained using molecular dynamics techniques and an opportunity to share data, similar to what structural biologists have achieved with the worldwide Protein Data Bank  12  (wwPDB), should generally help to improve the quality, reproducibility, statistics, and reuse of the published results.
be/2S3qjBIE6Y4) This creates divisions within the community and makes it difficult to write workflows that function equally well across a number of force fields and a number of different software packages. One of the solutions to this problem is the development of programs that convert/handle molecular information between the different file formats such as VMD, 32 cpptraj,  33  MDAnalysis,  34, 35  mdtraj,  36  LOOS,  37, 38  and many others for trajectory analysis and TopoGromacs,  39  CHARMM-GUI,  40  CHAM-BER,  41  ParmEd (http://parmed. github. io/ParmEd/html/ index. html#), InterMol  42  (https://github. com/shirtsgroup/ InterMol), and others for topology generation and editing. The aim of these programs is to translate as much information as possible from one molecular file format into another.
One recent example is BioSimSpace (https://biosimspace. org/), which provides wrappers that simplify the generation of the command files that are used to control the running of simulations. This allows researchers to write workflows that are independent of the choice of the underlying packages used to perform the simulation. BioSimSpace aims to run all stages of the workflow using the simulation software installed on the researcher's computer that is compatible with the force field chosen for the specific calculation. While translators and program wrappers like ParmEd and BioSimSpace solve some of these problems, they are not a universal solution. They do not solve the issue that different simulation programs use different algorithms (or interpretations of algorithms, for example, different implementations of thermostats or integrators) or that different programs store and represent molecular information in different ways (e.
The emergence of dedicated tools is now helping to answer to the how question. Software such as MDsrv,  47  HTMoL,  48  Mol* (https://molstar. org), and Molmil  49  are now taking advantage of the WebGL API for sharing trajectories through interactive visualization on the web. 50 ther fields of research can help us to answer to the where question. Existing databanks, such as wwPDB  51  and Galaxy (https://usegalaxy. org), have been recognized by the scientific community. However, the establishment of an analogous, specialized platform for MD data, poses a great challenge, given the current lack of long-term support for the infrastructure projects of this kind. It is not clear yet who should be responsible for building such platform and how this infrastructure could be funded in a sustainable way, preferably without relying on shortterm research grants, to cover the costs of development, maintenance, and data hosting.
Journal of Chemical Information and Modeling Perspective driven, special-purpose platforms like the GPCRmd (http:// www. gpcrmd. org), Lipidbook  52  and NMRlipids  45  (http:// nmrlipids. blogspot. com), Ligandbook,  53  MoDEL,  54  and BIGNASim 55 lead the way, providing specialized platforms for deposition and analysis of G protein-coupled receptors (GPCR), lipids, small molecules, proteins, and nucleic acids, respectively. General data sharing resources like Zenodo (https://zenodo. org), FigShare (https://figshare. com), Open Science Framework (https://osf. io), and others also provide an opportunity for every researcher to deposit their simulation files and trajectories. Nevertheless, those resources may, sometimes, not provide enough space to sustainably store MD simulations outputs (with file size limits ranging between 5 and 50 GB).
Practical challenges come from the size of data sets. However, one can envision sharing at least minimal data sets to improve the following: • Methods reproducibility. Provide sufficient details to replicate the study; this is in principle already done in publications, but authors, reviewers, and editors should pay special attention to the question, and direct sharing of all input files should be mandatory, • Raw data reproducibility. Share a minimum amount of data in the form of MD simulation snapshots or, even better, whole trajectories on existing data sharing repositoriesZenodo, Figshare, OSF. • Results and inferential reproducibility: Share among other analysis code, pipeline/workflow and example used. Inspiration can be found in other research fields (e. g. , genomics  56  or proteomics  57  ), and existing dedicated initiatives, like MemProtMD 58 (http://memprotmd.
■ ACKNOWLEDGMENTS This work was supported by  BioExcel Centre of Excellence  (www. bioexcel. eu)funded by the  European Union  contracts  H2020-INFRAEDI-02-2018-823830  and  H2020-EINFRA-2015-1-675728. M. C. acknowledges support from  CNRS-MITI  grants  PEPS MPI 2018  and "Modeĺisation du vivant" 2019. This work was supported by grants from the  Gustafsson Foundation and Science for Life Laboratory  to L. D. H. G. has been supported by  Max Planck Society  and the  German Research Foundation (DFG) ,  Cluster of excellence Multiscale Imaging , and the  DFG priority programmes 1648 ,  755 , and  803. O. H. S. O. acknowledges financial support from  Academy of Finland  ( 315596 ). D. G. A. S. thanks the  National Science Foundation  for support under Grant No. ACI-1547580. P. J. S. would like to thank  Wellcome  [ 208361/Z/17/Z ], the  BBSRC  [ BB/P01948X/1 ,  BB/R002517/1 ,  BB/S003339/1 , and  BB/ I019855/1 ] and  MRC  [ MR/S009213/1 ] for funding.
Financial support: project No. QJ1510353  (the  Ministry of Agriculture of the Czech Republic ); project  NPU I ,  LO1417  (the  Ministry of Education, Youth and Sports of the Czech Republic) ; the  EC  ( CZ. 1. 05/2. 1. 00/01. 0017 ); the  CAS  (RVO: 68081731 ), the  Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences  (RVO:  61388963 ), and the  Czech Science Foundation  ( 19-03909S ). JS is supported by  AgreenSkillsPlus  ( PCOFUND-GA-2013-609398 ) and  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ). We thank  Dr. Kelly Hennessey  for proofreading of the manuscript.
Interacting proteins and protein domains coevolve on multiple scales, from their correlated presence across species, to correlations in amino-acid usage. Genomic databases provide rapidly growing data for variability in genomic protein content and in protein sequences, calling for computational predictions of unknown interactions. We first introduce the concept of direct phyletic couplings, based on global statistical models of phylogenetic profiles. They strongly increase the accuracy of predicting pairs of related protein domains beyond simpler correlation-based approaches like phylogenetic profiling (80% vs. 30-50% positives out of the 1000 highest-scoring pairs). Combined with the direct coupling analysis of inter-protein residue-residue coevolution, we provide multi-scale evidence for direct but unknown interaction between protein families.
Introduction Essential to life at the molecular level is the interplay of molecules and macromolecules. Interactions contribute to diversity and coordination of reactions to accomplish feats that would be impossible if all parts worked fully in isolation. Proteins are no exceptions and many of them undergo concerted interactions to achieve their full potential. Many interactions have been described in detail, including inter-and intra-protein domain-domain interactions, which will be the focus of this work. However, many more meaningful interactions await to be discovered and explored. An issue with the experimental description of such interactions is that many are transient and that high-throughput technologies to identify such interactions are very error prone  [1]. Advances in sequencing technology and the subsequent accumulation of vast sequence databases have fueled the generation of mathematical frameworks which aim to identify protein-protein interactions  [2, 3].
Phyletic couplings improve the prediction of domain-domain relationships beyond correlations The analysis starts with a fairly standard construction of phylogenetic profiles  [5] , as outlined in Fig 1. Multiple-sequence alignments are needed at a later stage to perform inter-protein DCA. Since Pfam MSA have been extensively used in this respect, the analysis is performed on the domain level  [30] , using Pfam  [31]  as the input database. Pfam is based on reference genomes and we use the 1041 bacterial ones. The bacterial model organism Escherichia coli is used as a reference, i. e. only the 2682 domain families existing inside the K12 strain of E. coli are considered (the Supplement S1 Text Fig K shows that the results are robust when expanded beyond this choice). Since our method is based on covariation of presence and absence of domains in genomes, only variable domains existing in at least 5% and at most 95% of the considered genomes are considered, leaving 2041 domains.