dataesr/scientific-paragraphs-categorization huggingface dataset:
-Post embryonic development Monitoring of post-embryonic development was performed as described by Olmedo et al. [25]. In brief, upon synchronization, 20 embryos per µl in M9 buffer overnight with agitation, allowing L1 larvae to hatch. A single L1 larvae was added per well in 96-well plates with S-Basal media, 10 g/L E. coli OP50 (wet weight) and 100 µM D-Luciferin. The luminescence was measured with a Berthold Centro LB960 XS 3 during 1 second at 5-min intervals, within a temperature controlled Panasonic MIR-154 incubator. -Statistics Statistical analysis was done using Graphpad, Excel, Numbers and R softwares. We consider statistical test to be significant when pValue <0. 01. elegans genes adsl-1, atic-1b and ppat-1. Expression of S. cerevisiae genes ADE13, ADE17 and ADE4 was used as positive control, and expression vector without insert as negative control.
Acknowledgements Some C. elegans strains were provided by the  CGC  (https://cgc. umn. edu), which is funded by  NIH Office of Research Infrastructure Programs  ( P40 OD010440 ). We thank Wormbase, which is supported by  National Institutes of Health (NIH)  grant  U41 HG002223. This work was supported by an  AFM-Téléthon Trampoline  grant (ref. 18313). RM is supported by  AFM-Téléthon  PhD scholarship (ref. 20450). RM stay in M. A-S. lab supported by a  Short Term Scientific Mission  financed by  GENiE  ( COST Action   BM1408 ). M. A-S and M. O are supported by the  Ramón y Cajal program  of the  Spanish Ministerio de Economía y Competitividad ,  RYC-2010-06167  and  RYC-2014-15551 , respectively. Confocal microscopy, and locomotion assay imaging were done in the  Bordeaux Imaging Center , a service unit of the  CNRS-INSERM and Bordeaux University , member of the national infrastructure  France BioImaging.
19 15 PPAT-1 Enzyme C. elegans Human % Similarity % Identity Yeast % Similarity % Identity 1 GPPAT PPAT-1 PPAT 63 46 ADE4 51 32 2 GART/AIRS F38B6. 4 (N-ter) ADE5,7 58 39 GART 62 44 3 GARS F38B6. 4 (C-ter) ADE8 51 30 4 FGARAT F10F2. 2 PFAS 62 47 ADE6 52 34 5 AIRC PACS-1 (C-ter) ADE2 47 32 PAICS 69 51 6 SAICARS PACS-1 (N-ter) ADE1 40 20 7 ADSL ADSL-1 ADSL 64 47 ADE13 61 43 8 ATIC ATIC-1 ATIC 75 60 ADE16/17 72 57 9 ADSS ADSS-1 ADSS 69 54 ADE12 70 51 10 AMPD AMPD-1 AMPD 72 59 AMD1 67 49 11 ADOD C06G3. 5 ADA 58 40 - - - 12 ADOK R07H5. 8 ADK 69 51 ADO1 60 43 13 APRT T19B4. 3 APRT 65 46 APT1 61 43 14 PNP K02D7. 1 PNP 64 47 PNP1 56 40 15 HGPRT HPRT-1 HPRT 62 49 HPT1 44 25 16 IMD T22D1. 3 IMPDH 73 57 IMD1 66 46 17 GMPS GMPS-1 GMPS 65 51 GUA1 51 36 18 GDA CPIN-1 GDA 38 22 GUD1 37 21 19 XO GAD-3 XDH 50 35 - - - 19 XO F15E6.
Aerobic respiratory growth generates endogenous reactive oxygen species (ROS). ROS oxidize protein-bound methionine residues into methionine sulfoxide. Methionine sulfoxide reductases catalyze the reduction of methionine sulfoxide to methionine in proteins. Here, we use high-throughput nanoLC-MS/MS methodology to establish detailed maps of oxidized proteins from Bacillus cereus ATCC 14579 ΔpBClin15 and its mutant for which the methionine sulfoxide reductase AB gene (msrAB) has been inactivated (Madeira  et al. , 2017) [1]. Lists of oxidized peptides and proteins identified at early exponential, late exponential and stationary growth phases are supplied in this article as data files. Raw data are deposited at the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers, PXD006169 and PDX006205 (http://www.
Value of the data This is the first dataset that provides a whole-cell global view of protein-bound methionine oxi- dation in B. cereus. The identified methionine sulfoxide peptides contribute to establish a map of oxidized proteins in this pathogen. This dataset allows visualizing the cellular and extracellular impact of methionine sulfoxide reductase MsrAB. This dataset should be useful in prioritizing proteins for further studies to determine their potential as redox regulator of virulence.
Data This proteomics dataset comprises the whole set of peptides and Met(O) peptides identified in the cellular proteome (Supplementary Table  1 ) and exoproteome (Supplementary Table  2 ) of Bacillus cereus ATCC 14579 ΔpBClin15 (Wild-type,  [2] ) and its mutant ΔmsrAB  [1]. A total of 8720 and 3417 Met(O)peptides were detected in the cellular proteomes and exoproteomes, respectively. Supplementary Tables  3  and 4  show the 174 identified oxidized cellular proteins that were classified in 19 functional classes and the 70 detected oxidized exoproteins that were classified in 14 functional classes. From Supplementary Tables  3  and 4 , we generated heatmaps to visualize clustered oxidized proteins in cellular (Supplementary Fig. 1 ) and exoproteome (Fig. 1 ), according to the growth phase. Global changes between the wild-type and ΔmsrAB mutant strains can also been observed.
LC-MS/MS and data analysis Peptide samples were analyzed on LTQ-Orbitrap XL hybrid mass spectrometer (ThermoFisher) coupled to an Ultimate 3000 nRSLC system (Dionex, ThermoFisher) as reported previously  [9]. Mass spectrometry analysis was carried out with full scans (300-to 1800 m/z) in data dependent mode using a TOP3 strategy consisting in a full MS scan followed by MS/MS analysis of the three most abundant peptide signals. MS/MS data obtained from all LC-MS analyses were searched against an inhouse polypeptide sequence database using MASCOT (version 2. 3. 02) search engine from Matrix Science. The parameters used for data analysis included trypsin as the protease with a maximum of two missed cleavages. Carboxyamidomethylation of cysteine (þ57. 0215) was specified as a fixed modification and oxidation of methionine (þ 15.
9949) as a variable modification. Mass tolerances were set at 5 ppm for precursor ions and 0. 5 Da for fragment ions. Peptides were considered as identified with a peptidic identity threshold below a p-value of 0. 05  [10]. Proteins were validated when at least two different peptides were detected. False-positive identification of proteins was estimated using the corresponding reverse decoy database as below 1% with these parameters. For comparative analysis only the proteins containing at least two oxidized peptides in two replicates of the same sample were considered as oxidized. Hierarchical clustering of oxidized proteins was carried out with PermutMatrix Software  [11].
For fluorescence microscopy, C23A E58C TMR Im9 was added in a 1. 3-fold molar excess over E9 DNase, with excess free immunity removed by gel filtration on a Superdex 75 column. Molecular Dynamics Simulations. The structure of OmpF trimer (PDB: 2OMF) embedded within a DPPC membrane was obtained from the MemProt MD database. 13 tructures of OBS1 and OBS2 peptides were generated using PyMOL. 14 The OBS peptide was placed in bulk solution approximately 5 nm above one OmpF subunit on the extracellular side. Steered MD simulations were performed whereby a harmonic spring was attached to the N-terminus of the OBS peptide and pulled at a constant velocity of 0. 01 nm• ps -1. The peptide was pulled through the OmpF pore into the periplasmic space until the distance between their centers of mass was around 10 nm.
Article The structure of ColE9 OBS1 peptide from Housden et al. 7  was docked onto OmpF trimer (PDB: 2OMF) embedded within a DPPC membrane. Missing atoms were added using PyMOL. The structure of the peptide in reverse orientation was manually built using a text editor on the basis of the positions of the peptide backbone in the crystal structure, and the side chains were later added using PyMOL. All systems underwent a 10 ns equilibration simulation with positional restraints applied to the heavy atoms of the protein and peptide. These restraints were removed during subsequent 100 ns production simulations, which were performed in triplicate. Isothermal Titration Calorimetry. ITC measurements were performed on a MicroCal iTC 200 thermostated at 25 °C with all proteins prepared in 20 mM potassium phosphate buffer pH 6.
15 μs/pixel), and a digital zoom of 10×. Images were recorded using Zeiss Zen 2011 software. Image processing and intensity quantification were obtained using ImageJ. A mask delimiting the contour of each cell was applied, and the mean intensity was determined after normalizing with background fluorescence. Single Channel Recordings in Planar Lipid Bilayers. Planar lipid bilayers were formed by using a solution of 1,2diphytanoyl-sn-glycerol-3-phosphocholine (DPhPC) (Avanti Polar Lipids, Alabaster, AL, USA) dissolved in pentane (5 mg mL -1 ) across a 100 μm diameter aperture in a 25 μm thick Teflon film. 15 Currents were recorded by using a patch clamp amplifier (Axopatch 200B, Axon Instruments, Foster City, CA, USA) with a sampling interval of 100 μs (10 kHz acquiring frequency). Data were filtered with a 2 kHz low-pass Bessel filter and digitized with a Digidata 1322 A converter (Axon Instruments) at a sampling frequency of 10 kHz.
Data analysis was performed with pClamp 10. 3 software (Molecular Devices). OmpF trimer (0. 5 μL of 33. 7 μM or 0. 2 μL of 66 μM protein in 20 mM Tris-HCl, pH 8. 0, 5 mM EDTA, 50 mM LiCl, 1% (w/v) β-OG) was added to the cis compartment (at ground) and incubated until a single porin had inserted into the bilayer. Currents were recorded after the cis chamber had been perfused with 20 mM potassium phosphate, pH 6. 5, 100 mM KCl, to remove excess protein. Both the cis and trans compartments of the apparatus contained 20 mM potassium phosphate, pH 6. 5, 100 mM KCl (1 mL), at 20. 5 ± 0. 5 °C. Before adding the (OBS) 3 constructs to either side of OmpF, the orientation of OmpF in the bilayers was defined by using an I-V curve plot. 16 With the defined positive asymmetry of OmpF, the binding of (OBS) 3 constructs to the extracellular side was monitored by adding the construct (50 nM) into the cis compartment at a holding potential of -100 mV.
Digital Activist Scores -Measuring Digital Engagement To triangulate the qualitative findings, a research team and I gathered original data from Tweets, Facebook posts, and website metrics of the organizations under study. These platforms dominated the literature and were the most commonly used public interfaces at the time of the study. 5 Data collection procedures involved writing scripts and code using the Facebook and Twitter Application Programming Interface (API). 6 We surveyed websites over 18 months to note any changes across six-month intervals, and we gave each group a score over that time period. Facebook data were based on the total time the organization was on the platform. For Twitter, the data derived from the total time the organization was on the platform for measurements of Tweets, following and follower numbers, but other data were not readily available for the entire time each group used Twitter, so we collected and averaged other measures of Twitter participation, such as mentions and hashtags, over a one-year period.
As a result, unionized teachers, social workers, and university faculty are categorized as middle/upper class. However, as a robustness check, I also conducted a separate quantitative analysis incorporating these groups as working class, rather than only highlighting intra-class differences of privilege  (Eidlin 2016). If a group met neither threshold, I considered it as mixed class. This mixed-class categorization of groups, which were not uniform in class composition, enabled me to determine if having a substantial portion of both classes tipped the balance in digital activism levels in either direction. This coding was based on interviews and queries during observations. For groups comprising public employees, this information was readily accessible publicly. For other organizations, I gathered data from staff and respondents regarding their members' employment statuses and used interviews to verify my initial classifications.
This article received support from the  National Science Foundation Doctoral Dissertation Improvement Grant  ( NSF DDIG SES-1203716 ), the  Jacob K. Javits Fellowship , the  Mike Synar Fellowship  from the  Berkeley Institute of Governmental Studies , the  Department of Sociology, University of California, Berkeley , and the  Berkeley Center for New Media. Support through the  ANR Labex IAST  is also gratefully acknowledged.
Data and simulated samples The searches described in this paper are performed using data from pp collisions recorded in 2016 by the CMS experiment at the LHC at a centre-of-mass energy of 13 TeV corresponding to an integrated luminosity of 35. 9 fb -1. Events in the search are collected based on p miss T triggers with thresholds ranging between 90 and 120 GeV. Additional control samples used for estimating backgrounds are selected by single-lepton triggers with p T thresholds of 24 and 27 GeV for muons and electrons, respectively. In this analysis, Monte Carlo (MC) simulation samples of SM processes are used to relate background yields in control and signal regions, to validate the background estimation methods based on data, and to predict contributions from rare processes. Simulated samples are produced using multiple generators.
5 cm. The "tight" criteria correspond to the final lepton selection of the analysis, described in Section 2. The probability that a loose lepton also passes the tight criteria, the tight-to-loose fraction TL , is measured as a function of lepton p T and |η| in an orthogonal "measurement region" largely dominated by multijet events, which is enriched in nonprompt leptons. The fraction TL is measured from data, after the subtraction of the simulated prompt lepton contribution. The final estimate of nonprompt leptons in a SR or control region (CR) is based on the observed yield in an "application region". The latter is defined in the same way as the corresponding SR or CR, with the exception that the lepton has to pass the loose lepton criteria but not the tight ones. The final estimate is obtained by scaling the data yield in the application region by TL /(1 -TL ), after subtracting the simulated prompt lepton contribution.
There is currently a lot of activity in R&D for future collider experiments. Multiple detector prototypes are being tested, each one with slightly different requirements regarding the format of the data to be analysed. This has generated a variety of ad-hoc solutions for data acquisition and online data monitoring. We present a generic C++11 online monitoring framework called DQM4HEP, which is designed for use as a generic online monitor for particle physics experiments, ranging from small tabletop experiments to large multi-detector testbeams, such as those currently ongoing/planned at the DESY II or CERN SPS beamlines. We present results obtained using DQM4HEP at several testbeams where the CALICE AHCAL, SDHCAL and SiWECAL detector prototypes have been tested. During these testbeams, online analysis using DQM4HEP's framework has been developed and used.
By developing tools which are designed from the beginning to be used for many different applications, the amount of effort and development time necessary to create data acquisition and monitoring setups can be reduced significantly, simplifying and speeding up planning and deployment of physics testbeams and allowing more science to be done faster. The AIDA-2020 project is an EU-funded project for advancing research and development infrastructure and technologies for particle physics detector development and testing, comprising 24 member countries and lead by CERN. The project is split into Work Packages; Work Package 5 is "Data acquisition system for beamtests", aiming to develop hardware and software to improve the infrastructure and tools available for testing new detector components in beams, especially for testbeams involving more than onedetector component.
II. THE DQM4HEP FRAMEWORK DQM4HEP is an online monitoring and data quality monitoring tool developed for physics testbeams for high-energy and particle physics. It is designed to be able to fulfil the requirements of monitoring for physics testbeams in a generic way. The structure of the program allows for independent components of the framework to be used, not used, or exchanged, by isolating each function of the program into specific and independent processes. The components that are specific to particular users -the analysis and standalone modules -are written in standard C++ code, meaning they are capable of performing any data unpacking, processing or analysis that is necessary. The framework then handles packaging this information in a useful way and networking to transmit it to where it is needed, meaning that the user does not have to worry about the mechanics of data storage, serialisation or transmission.
A. Prerequisites and dependencies DQM4HEP is written in the C++11 standard and requires ROOT5  [1]  for handling ROOT objects such as plots, charts and histograms. The visualisation package that contains code for the graphical user interfaces requires Qt4 [2]. DIM is used for network communication  [3]. There is also an optional dependency for the usage of the LCIO event data model, which is defined as the standardised filetype within AIDA-2020. If LCIO files are being used, DQM4HEP requires the LCIO software (part of the ilcSoft package  [4] ) in order to compile the libraries that enable serialisation of LCIO data.
Multiple event collector processes can be run in parallel if desired. The monitor element collector receives monitor elements from the DQM modules and makes them available to the monitoring interface, and are the last exit point of data before the GUI. Again, multiple monitor element collectors can be run if desired. There are two varieties of DQM modules: • Analysis modules receive events from the DAQ system via the event collector and process the event data into a form useful for monitoring, then encapsulate these as monitor elements to be sent to the monitor element collector. The analysis modules must be written for the specific usage being implemented but are produced from templates in which ordinary C++ code is used. • Standalone modules are almost identical to analysis modules, except that they receive data from somewhere that is not the DAQ system.
V. IMPLEMENTATION EXAMPLES DQM4HEP has already been used in a number of testbeams for multiple detector prototypes, including combined testbeams. So far these have been with the AHCAL+beam telescope, and SDHCAL+SiWECAL. More information on these detectors and testbeams can be found in the references  [5]   [6]  and some examples of the framework in use can be seen in Figure  2. In the two examples, interfacing with the data acquisition system was done differently. For SDHCAL testbeams, DQM4HEP was interfaced with the DAQ using shared memory (shm), allowing it to access information online. For AHCAL testbeams, the framework was used in "nearly-online" mode -completed runs in LCIO format were accessed by the LCIO file service over network-attached storage as soon as an individual run file was finished.
VI. INTERFACE WITH GENERIC DATA ACQUISITION SOFTWARE FRAMEWORKS One of the AIDA-2020 Work Package 5 main tasks (Task 5. 3) is the "Development of central DAQ software and run control". The efforts in this Task 5. 3 have been focused in the developping of the EUDAQ  [7]  framework. EUDAQ was originally designed as data acquisition software for EUDETtype beam telescopes, EUDAQ has grown to become a generic DAQ framework for other detector types. EUDAQ is designed so that the core is flexible and portable, and all hardwarespecific components are separate and can be created, used or ignored at the user's discretion. The distributed process structure of EUDAQ allows individual elements to be swapped out, saving effort and development time, compared with custom-writing an ad hoc solution that has limited flexibility and portability.
While EUDAQ has an online monitoring component, it is not being discussed, and may be removed from future versions in favour of DQM4HEP. EUDAQ can be used as a generic DAQ, while DQM4HEP can be used as a generic data quality monitoring tool. Both are hardware-independent and when used in concert may form a fullyfeatured, generic and portable DAQ/DQM system, replacing most software used during beam tests. Development of an online linkage is underway, which will allow EUDAQ to stream events to DQM4HEP processes online. Once this is completed, the combined EU-DAQ/DQM4HEP system will allow a fully-generic DAQ and monitoring system. The only detector specific components will be: • EUDAQ Producer and DataConverterPlugin • Event type and serialisation method • Online anaylsis tasks and modules VII.
CONCLUSION With its generic and flexible programming, DQM4HEP forms a powerful and portable framework for online monitoring and data quality monitoring for physics testbeams that will allow physicists to focus on the physics goals instead of the engineering and software issues of building and deploying their own monitoring systems. Planned future work to allow DQM4HEP to interface directly with EUDAQ, a generic data acquisition system within AIDA-2020, will allow the two software frameworks to work together to form a generic data acquisition, monitoring and quality monitoring system that is capable of being used for nearly any type of detector model. The DQM4HEP framework can be found on Github at https://github. com/DQM4HEP. User and technical documentation is currently in progress but any enquiries about the framework can be directed to dqm4hep@gmail.
ACKNOWLEDGMENT This project has received funding from the  European Union 's  Horizon 2020 Research and Innovation programme  under Grant Agreement no. 654168. The research leading to these results has received funding from the  People Progr amme  ( MarieCurie Actions ) of the  European Unions Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement n. PCOFUND-GA-2013-609102, through the  PRESTIGE programme  coordinated by  Campus France.
After 12-14 hours, 10,000 C8161-GFP, OMM 2. 3-GFP, OMM 2. 5-GFP or Hermes 2B-GFP cells were carefully plated on the endothelial tubules network. The co-cultures were then incubated approximately 2 hours at 37 °C and subsequently imaged up to 24 hours, using IncuCyte ® S3 Live-Cell (ESSEN Bioscence), employing a dry 20 × objective. Images were then processed with the Incucyte, ImageJ and Adobe Illustratore software. Human cancer cells injections in zebrafish embryos. Before injections, zebrafish embryos were kept at 28 °C and manually dechorionated few hours before the injection. Melanoma cells were grown to 80-90% confluency, washed one time with PBS and trypsinised (0. 25% trypsin/0. 53 mM EDTA) to obtain a single cell suspension. Cells were then centrifuged for 5 minutes at 1000 g and resuspended in RPMI-1640 (for uveal melanoma lines and Hermes 2B) or DMEM medium (for cutaneous melanoma lines), in order to reach a final concentration of 6,000-10,000 cells/μl.
Microscopy. Starting from 30 hours post injection (hpi), embryos showing melanoma cells that had migrated were selected using a Leica MZ FLIII stereomicroscope (Leica) equipped with a Leica DFC310FX digital camera (Leica). Zeiss LSM 700 confocal microscope or Zeiss LSM 880 microscope (Zeiss) were used, employing a 25 × oil, a 40 × water immersion or a 63 × water immersion objective. For confocal microscopy, larvae were anesthetised in egg fish water containing 0. 02% tricaine, immobilised in 1. 2% low-melting agarose and then imaged up to 12 hours (one acquisition every 15-25 minutes), using 488 and 568 nm lasers. Z-volumes were acquired with a 1-to 2-μm resolution and images were processed using ImageJ, Imaris-Bitplane, Zen Blue and Adobe Illustrator software.