dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are very grateful to  Iskra Katic  for excellent technical assistance, and the advanced imaging and microscopy FMI technology platform. Some strains were provided by the  CGC , which is funded by  NIH Office of Research Infrastructure Programs  ( P40 OD010440 ). The work in R. C. lab is supported by the  European Cooperation in Science and Technology SBFI  grant  [C15. 0038 ]( SNF  project  174619 ), the  National Science Centre  grant  [2015/19/B/NZ3/02412 ], an  EMBO   Installation Grant , and the  Foundation for Polish Science  grant  [TEAM/2016-2/11 ], co-financed by the  European Union Regional Development Fund  within the framework of the  Operational Program Innovative Economy. The  Friedrich Miescher Institute for Biomedical Research  is sponsored by the  Novartis Research Foundation.
Work in the laboratory of L. P. is supported by the  Fondation pour la Recherche Medicale Equipes FRM  [ DEQ20140329538 ]. Y. T. is supported by a post-doctoral fellowship from the  Labex 'Who am I?' Laboratory of Excellence No. [ ANR-11-LABX-0071 ], the  French Government through its Investments  for the  Future program  operated by the  French National Research Agency (ANR)  under Grant No. [ ANR-11-IDEX-0005-01 ].
Structural Elucidation of the Compounds Natalenamide A (1) was acquired as an amorphous powder. The molecular formula of 1 was determined to be C 19 H 25 N 3 O 5 from the sodium-adducted molecular ion at m/z 398. 1691 [M + Na] + (calculated for C 19 H 25 N 3 O 5 Na, 398. 1692) using positive-ion mode high-resolution electrospray ionization mass spectrometry (HR-ESIMS) data. The infrared (IR) spectrum showed a strong absorption band at 1670 cm -1 , suggesting the presence of amide groups in the molecule. The detailed analysis of 1 H NMR data of 1 (Table  1)       H COSY, HSQC, and HMBC experiments) revealed the presence of three amino acids in 1: glutamate (Glu), phenylalanine (Phe), and valine (Val) (Figure  2 ). The connectivity of these amino acids was determined by the HMBC correlations of H-1/C-5 and C-19, H-11/C-10 and C-19, and H-6/C-5 and C-10 (Figure  2 ).
2032 [M + H] + (calculated for C20H28N3O5, 390. 2029) and 412. 1849 [M + Na] + (calculated for C20H27N3O5Na, 412. 1848), respectively. Compared with the molecular formula and NMR spectral data of 1, compound 2 appeared to have an additional CH2 group in the peptide skeleton. Comprehensive scrutiny of the one-dimensional (1D) ( 1 H and 13 C NMR) and 2D NMR ( 1 H-1 H COSY, TOCSY, HSQC, and HMBC) spectra of 2 revealed the existence of three amino acid residues: Glu, Phe, and Leu (leucine). The sequence of these amino acids was constructed based on the HMBC correlations of H-1/C-6 and C-20, H-12/C-11 and C-20, and H-7/C-6 and C-11 (Figure  2 ). To determine the absolute configuration of 2, derivatization of the Glu, Phe, and Leu residues with L-FDAA was performed and then analyzed by LC/MS. In the LC/MS data, L-Glu, L-Phe, and L-Leu were absolutely detected by comparing the retention times for the L-FDAA derivatives of 2 with those of the standard amino acids.
Sequencing and Species Identification Sequences were assessed for purity and mismatches using BioEdit  [22]. The forward and reverse sequences obtained for each strain were assembled with BioEdit and tested for chimeras using DECIPHER (http://decipher. cee. wisc. edu/FindChimerasOutputs. html). Resulting sequences were deposited in GenBank (accession number: KY558684). Blast analyses with almost-complete 16S rRNA sequences (1368 bp) were performed using the National Center for Biotechnology Information (NCBI) database (reference RNA sequences). The results indicated that strain RB99 is a member of the genus, Actinomadura. Sequences of the first 10 hits were downloaded from the NCBI database and aligned with the 16S rRNA sequence of Actinomadura sp. RB99 using the Sina sequences alignment service  [23].
Two different phylogenetic trees were reconstructed with neighbor-joining or maximum-likelihood algorithms using MEGA software version 7. 0. 26 [24] [25] [26]. The evolutionary distance model of Tamura and Nei was used to generate evolutionary distance matrices for the maximum-likelihood and neighbor-joining algorithms, with deletion of complete gaps and missing data  [27]. For the maximum-likelihood algorithm, discrete gamma distribution was used (+G), and the rate variation model allowed for some sites to be evolutionarily invariable (+I). For the neighbor-joining algorithm, the rate variation among sites was modeled with a gamma distribution (+G). The confidence values of nodes were evaluated by bootstrap analyses based on 1000 resampling steps  [28].
Funding: This work was supported by the  National Research Foundation of Korea (NRF)  grant funded by the  Korea government (MSIT)  ( 2018R1A2B2006879 ) to K. H. K. We are grateful for financial support from the  German Research Foundation  ( CRC1127   ChemBioSys  and  BE 4799/3-1 ) and the  Daimler Benz Foundation  to C. B. R. B. is generously supported by the  International Leibniz Research School for Microbial and Biomolecular Interactions (ILRS)  and the  School for Microbial Communication (JSMC, DFG). This work was also supported by the  Nano Convergence Industrial Strategic Technology Development Program  ( 20000105 ,  Development of Cosmeceutical Material Platform using Organo-Nano Complexes based on Natural Active Compounds)  funded  By the Ministry of Trade, Industry & Energy (MOTIE, Korea).
After the burst of granule formation, the number of ArtiG mCh gradually decreased as they underwent fusion. Concurrently with the nucleation of the first ArtiG mCh , the cytosolic fluorescence intensity (dilute phase) stopped increasing to eventually reach a stationary phase, whereas the total cytoplasmic fluorescence intensity (dilute + condensed phases) kept increasing over time. These results suggest that the nucleation of ArtiG mCh occurs at a critical concentration of cytosolic mCherry-FFm, referred to as apparent nucleation threshold in the Fig. 1e  and Supplementary Fig. 1e , which is a signature of first-order phase transitions. Furthermore, ArtiG mCh seem to establish a dynamic equilibrium buffering the variations of FFm concentration in the cytoplasm. Taken together, these data illustrate that FFm scaffold undergoes a phase transition to form ArtiG micrometric condensates within the cytoplasm of living cells, in a concentration-dependent manner.
2c ). In contrast, micrometric ArtiG mCh tend to disappear within few minutes upon addition of FK506, a specific competitor of the Fm-Fm interaction  26  , to the cell culture medium. This demonstrates that Fm-Fm interactions are essential to maintain the integrity of the condensates. Concomitantly to ArtiG mCh dissolution, a large increase of the cytosolic fluorescence was observed, indicating that ArtiG are comparable to protein reservoirs (Fig. 2f  and Supplementary Fig. 2d ). Taken together, these data demonstrate that ArtiG mCh recapitulate the hallmarks of liquid-like behavior described for native RNP granules, as they are spherical and relax into one spherical droplet after fusion and deformation, but also as they dynamically exchange components with the dilute cytosolic phase. Moreover, despite their sensibility to Fm-Fm homodimer disruption, ArtiG mCh resist dilution as seen for many cellular condensates, illustrating that stable assemblies can form from weak interactions.
Acknowledgements We acknowledge all the members of the  Pole de Chimie Biophysique ,  A. Hubstenberger ,  D. O. Wang ,  H. Saito , and  J. F. Joanny  for fruitful discussions. We particularly thank  A. Blanch Jover ,  E. Ipenday , and  M. Bénard  for their help along the project, as well as  A. G. Tebo  and  A. Colin  for carefully reading the manuscript. We also thank  M. W. Hentze  and  T. Fujita  for kindly providing PABP-EGFP and Pumilio 1 plasmids. M. G. J. N. was supported by  FRM  ( ING20150532742 ). S. K. was supported by a  French government fellowship , R. C. by  ANR  ( ANR-14-CE09-0013-01 ). This work was supported by the  CNRS, Ecole Normale Supérieure , the  Association pour la Recherche sur le Cancer  ( 20181208003 ), and the  HFSP   Program Grant  ( RGP0050/2014 ) to Z. G. and  ANR  ( ANR-14-CE09-0013-01 ) to D.
Bacterial strains and plasmids All experiments were carried out with E. coli DH5αE. TEM alleles were ligated into the pACSE3 plasmid  (Barlow and Hall 2002) , which contains a tetracycline resistance cassette, to render the pACTEM plasmid and electrotransformed in E. coli as previously described  (Schenk et al. 2012). TEM is then under the control of the pTac promotor, and to induce expression 50 μM isopropyl β-D-1-thiogalactopyranoside (IPTG) is added. The TEM-1 sequence is identical to that present in pBR322 (GenBank accession J01749).
Ponceau staining was then used to visualize the total protein on the blots. After removing the Ponceau staining, TEM was detected by western blot with a mouse monoclonal antibody against TEM-1 (AbCam antibody 122251), using a goat anti-mouse horseradish peroxidase as a secondary antibody (Bio-Rad 170-6516). On every blot we included a reference sample of TEM-1, obtained from aliquots of a stationary phase TEM culture. We used Bio-Rad Image Lab 2. 01 software to determine the TEM signal (TEM band intensity in the Western) and the total protein content (the sum of all band intensities detected by the Ponceau staining), and the quotient is the relative TEM expression. This ratio was then normalized by the TEM-1 reference signal, to account for any differences in signal intensity due to minor experimental variation.
Measurement of resistance to CTX To measure CTX resistance of TEM alleles, we determined the inhibitory concentration killing 99. 99% (IC 99. 99 ,  Schenk et al. 2012). For this assay, un-induced (i. e. , no IPTG) exponential phase cells are plated out at different cellular densities (200, 2 × 10 4 , and 2 × 10 6 cells/6 cm plate) on plates with solid medium supplemented with 15 μg/mL tetracycline, 50 μM IPTG, and different CTX concentrations (0. 015625-0. 5 μg/mL CTX, by twofold increments). Colonies are counted after 40 h of incubation at 37 °C. In previous work, we determined the IC 99. 99 antibiotic concentration by linear interpolation between the two concentrations and plate densities straddling this point. Here we refined this analysis, by fitting a dose response model to all the data using a maximum likelihood method, and inferring the IC 99.
Abstract. The first observing run of Advanced LIGO spanned 4 months, from September 12, 2015 to January 19, 2016, during which gravitational waves were directly detected from two binary black hole systems, namely GW150914 and GW151226. Confident detection of gravitational waves requires an understanding of instrumental transients and artifacts that can reduce the sensitivity of a search. Studies of the quality of the detector data yield insights into the cause of instrumental artifacts and data quality vetoes specific to a search are produced to mitigate the effects of problematic data. In this paper, the systematic removal of noisy data from analysis time is shown to improve the sensitivity of searches for compact binary coalescences. The output of the PyCBC pipeline, which is a python-based code package used to search for gravitational wave signals from compact binary coalescences, is used as a metric for improvement.
These detections were part of a broader search for CBC signals carried out by multiple search pipelines during O1  [5, 6, 7, 8, 9, 10]  and searches for unmodeled transients  [11, 12, 13, 14]. Searching for gravitational waves requires an understanding of instrumental features and artifacts that can adversely affect the output of a gravitational wave search pipeline. Throughout the observing run, noisy data were identified in the form of data quality (DQ) vetoes to ensure that the analysis pipelines did not analyze data known to be contaminated with excess noise  [15]. These vetoes are discussed further in Section 4. This study measures the effects of removing data with excess noise on the output of PyCBC  [9, 5, 10] , a python-based pipeline used to search for CBC signals. Section 3 contains a brief description of the PyCBC search pipeline and its internal DQ features.
Section 2 outlines the data selection and noise characterization processes. The DQ vetoes that are generated in the noise characterization process are described in Section 4. The methodology of this study is discussed in Section 5. This paper focuses on two specific subsets of the O1 data set. The first data set, from September 12 -October 20, 2015, was used for background estimation for GW150914. This data set is discussed in Section 6. The second data set, from December 3, 2015 -January 19, 2016, was used for background estimation for GW151226. This data set is discussed in Section 7. Section 8 describes the limiting noise sources for CBC searches.
Data Selection The strain data measured at the output of the detectors are typically non-stationary and non-Gaussian and contain noise artifacts of varying durations. The longer duration non-stationary data can affect the overall sensitivity of the search, but they do not result in loud background events as they occur on a timescale of hours. The transient noise artifacts, however, occur on a timescale of seconds and can reduce the sensitivity of CBC searches by producing loud background events. Data quality studies must be performed to search for causes of transients in the data that generate loud events in a gravitational wave search. If the source of noise is identified, a veto is generated to flag times when transient noise makes the data unsuitable for analysis. Section 4 describes DQ vetoes that are used to indicate when the detector data are known to have excess noise  [16, 17, 15, 18].
The PyCBC search pipeline The PyCBC pipeline is designed to search for gravitational wave transients from CBCs  [5]. It employs a matched filter algorithm, which correlates expected CBC waveforms with detector data and outputs a ranking statistic, the signal-to-noise ratio (SNR). If the ranking statistic exceeds a specified threshold, an event, or "trigger", is generated. The SNR of each trigger is weighted based on a signal consistency test  [19] , resulting in a refined ranking statistic called re-weighted SNR. Section 3. 1 discusses this signal consistency test further. To perform this search, the matched filter algorithm needs to know what to search for. A collection of model CBC waveforms is generated before the analysis  [20, 21]. Each of these waveforms is called a template and the full collection of waveforms is referred to as the template bank.
Any triggers that are found in coincidence with the same source parameters in both detectors represent potential gravitational wave signals and are referred to as foreground events. Some of these foreground events will be chance coincidences between noise in each detector, which is expected given the number of events in each data set. To determine the statistical significance of foreground events, a background distribution is generated using a time shift technique  [5]. The statistical significance of any candidate gravitational wave is then quantified by calculating the rate of background events from detector noise that are at least as loud as the candidate event. This statistic is called the false alarm rate (FAR). Any loud triggers that appear as the result of instrumental transients will extend the background distribution and the influence the measured false alarm rate.
Measuring the Effects of Data Quality Vetoes To test the effects of DQ vetoes, the PyCBC search pipeline was run with and without applying vetoes. The only vetoes that were used in all runs are those that indicate that the data were not properly calibrated, that a data dropout occurred, or that there were test signals being injected into the detectors. Gating is internal to the search pipeline and was applied in all of the analyses. Two methods were used to understand the effects of applying vetoes. The first, described in Section 5. 1, considers the average sensitivity of the search pipeline to gravitational wave signals. The second, described in Section 5. 2, compares the measured search backgrounds and the false alarm rates of recovered gravitational wave signals.
Measuring search sensitivity The metric used to measure the sensitivity of the search pipeline is sensitive volume. Sensitive volume is measured by injecting simulated gravitational wave signals into the data and attempting to recover them using the search  [5]. The ability of the pipeline to recover signals at a given false alarm rate is then measured by analyzing the number of missed and recovered injections. In addition to the sensitive volume, the amount of time used in the analysis must be considered when removing noisy data. If a search is rejecting too much data, it will miss the opportunity to detect signals. To address this, the sensitive volume of the search is multiplied by the amount of analysis time to create a new metric called VT. If time is removed from an analysis, the sensitive volume of the search must increase to make up for the shorter analyzed time.
Comparing search backgrounds In the first observing run, the bank of CBC waveform templates used in the PyCBC search was divided into three bins  [22]. The significance of any candidate gravitational wave found in coincidence between the two detectors is calculated relative to the background in its bin. Waveforms with different parameters will respond to instrumental transients in different ways. This binning is performed so that any foreground triggers are compared to a background generated from similar waveforms. As such, the effects of removing data from the PyCBC search are variable depending on which bin is considered. The actual gravitational wave signals discovered in the PyCBC search, GW150914 and GW151226, were part of a full search that was broken into 3 bins but reported as a single table of results.
Search sensitivity To measure the effects of DQ vetoes on the sensitivity of the search, the analysis containing GW150914 was performed with and without applying data quality vetoes. The resulting measurements of VT were divided to calculate a VT ratio. Figure  2  shows the change in VT when vetoes are applied for two values of IFAR and several chirp mass bins. The lowest chirp mass bin contains BNS signals and does not show any improvement in sensitivity when DQ vetoes are applied. This is discussed further in section 6. 2. Since the bulk and edge bins contain systems with a large range of masses that can respond in different ways to instrumental artifacts, these higher mass bins have been split for sensitivity estimation. The higher mass bins in Figure  2  are binned linearly in ln(M). The higher chirp mass bins show an improvement in search sensitivity for both values of IFAR.
BNS bin Binary neutron star systems have the longest waveforms used in the search pipelines. Since these signals spend ∼ 10 -100 seconds in LIGO's sensitive band, the χ 2 test is effective at discriminating between binary neutron star signals and transient noise, which have a duration of ∼ 1 second. Figure  3  shows the background distribution of the BNS bin in the PyCBC search for the analysis containing GW150914. As expected, since waveforms in the BNS bin are not as susceptible to instrumental transients, the background distribution does not change significantly when noisy data are removed from the analysis.
Conclusions Data quality vetoes improved the sensitivity of the PyCBC search in Advanced LIGO's first observing run. Although the sensitivity of the search to BNS signals was not dramatically affected, VT improved significantly for higher mass sources when DQ vetoes are applied. The gravitational wave signal GW150914 was strong enough that it was louder than all background events regardless of what data were removed from the search. As such, DQ vetoes did not improve its significance. The false alarm rate of LVT151012, which occurred during the same analysis period, was improved from 0. 69 yr -1 to 0. 33 yr -1 when vetoes were applied. The false alarm rate of the second gravitational wave signal discovered in O1, GW151226, was decreased by over two orders of magnitude when DQ vetoes were applied, which resulted in a clear detection.
and  Advanced LIGO  as well as the  Science and Technology Facilities Council (STFC)  of the United Kingdom, the  Max-Planck-Society (MPS) , and the  State of Niedersachsen/Germany  for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the  Australian Research Council. The authors gratefully acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche Scientifique (CNRS)  and the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , for the construction and operation of the Virgo detector and the creation and support of the EGO consortium. The authors also gratefully acknowledge research support from these agencies as well as by the  Council of Scientific and Industrial Research of India , the  Department of Science and Technology, India , the  Science & Engineering Research Board (SERB), India , the  Ministry of Human Resource Development, India , the  Spanish Agencia Estatal de Investigación , the  Vicepresidència i Conselleria d'Innovació ,  Recerca i Turisme  and the  Conselleria d'Educació i Universitat del Govern de les Illes Balears , the  Conselleria d'Educació, Investigació, Cultura i Esport de la Generalitat Valenciana , the  National Science Centre of Poland , the  Swiss National Science Foundation (SNSF) , the  Russian Foundation for Basic Research , the  Russian Science Foundation , the  European Commission , the  European Regional Development Funds (ERDF) , the  Royal Society , the  Scottish Funding Council , the  Scottish Universities Physics Alliance , the  Hungarian Scientific Research Fund (OTKA) , the  Lyon Institute of Origins (LIO) , the  National Research, Development and Innovation Office Hungary (NKFI) , the  National Research Foundation of Korea, Industry Canada  and the  Province of Ontario  through the  Ministry of Economic Development and Innovation , the  Natural Science and Engineering Research Council Canada , the  Canadian Institute for Advanced Research , the  Brazilian Ministry of Science, Technology, Innovations , and Communications, the  International Center for Theoretical Physics South American Institute for Fundamental Research (ICTP-SAIFR) , the  Research Grants Council of Hong Kong , the  National Natural Science Foundation of China (NSFC) , the  Leverhulme Trust , the  Research Corporation , the  Ministry of Science and Technology (MOST), Taiwan  and the  Kavli Foundation.
We present a novel Lyman alpha (Lyα) radiative transfer code, SEURAT, where line scatterings are solved adaptively with the resolution of the smoothed particle hydrodynamics (SPH). The radiative transfer method implemented in SEURAT is based on a Monte Carlo algorithm in which the scattering and absorption by dust are also incorporated. We perform standard test calculations to verify the validity of the code; (i) emergent spectra from a static uniform sphere, (ii) emergent spectra from an expanding uniform sphere, and (iii) escape fraction from a dusty slab. Thereby we demonstrate that our code solves the Lyα radiative transfer with sufficient accuracy. We emphasise that SEURAT can treat the transfer of Lyα photons even in highly complex systems that have significantly inhomogeneous density fields.