dataesr/scientific-paragraphs-categorization huggingface dataset:
Q1 and Q2 resolutions were set at 0. 7 full width at half maximum (FWHM), whereas collision gas (CID, argon) was set to 2 mTorr. Identification of the compounds was based on precursor ion, selective fragment ions, and retention times. Selection of the monitored transitions and optimization of collision energy and radio frequency (RF) lens parameters were manually determined (see Table  1  for details). Qualification and quantification were performed in multiple reaction monitoring (MRM) mode using Quan Browser software (Thermo Scientific). Limits of detection and of quantification for each compound are indicated in Table  2. All amounts of opiates present in samples fit within the standard curve limits. Precision values were <1% for same-day measurements and <5% for inter-day measurements.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by  Inserm ,  CNRS  ( Postdoctoral fellowship  to ALB),  University of Strasbourg ,  French Ministe`re de´le´gue´a`la Recherche et a`l'Enseignement Supe´rieur  (PhD fellowship to ALB, MA, and  IW ITMO Cancer (YG) ,  National Council for Scientific Research of Lebanon (CNRS-Lebanon , PhD fellowship to JM),  National Research Foundation of South Africa (NRF , PhD fellowship to TM), and  USIAS (AC).
These data were made available in several public databases 6 , thus providing to biologists in the field an accurate subcellular (and subplastidial) localization for many chloroplast proteins. This was especially true for envelope proteins whose identity and location remained mostly unknown before these analyses, since envelope membranes represent a minor chloroplast component (1-2% of the chloroplast proteins) while playing a key role in chloroplast metabolism and biogenesis  5, 20. Using the protocol described here, we recently analyzed the composition of the three main chloroplast compartments from Arabidopsis (i. e. , the stroma, the thylakoids, and the envelope membrane system) 9. Based on a semi-quantitative proteomics approach (spectral counting), we were able to assess the partitioning of hundreds of proteins in these three chloroplast compartments.
Acknowledgements This work was supported by a joint PhD fellowship to IB from the  INRA Plant Biology and Breeding Division  and from the  Labex GRAL  ( ANR-10-LABX-49-01 ). We would also like to acknowledge the  ANR  project  ANR-15-IDEX-02 ,  Dr. Olivier Vallon  ( IBPC Paris ) for antibodies against LHCP and  Dr. Renaud Dumas  ( LPCV, Grenoble ) for antibodies against KARI.
We used P inaA to control expression of yellow fluorescent protein (yfp) and P acrAB to control red fluorescent protein (rfp). By simultaneously measuring the dynamics of all three fluorophores (P marA -cfp, P inaA -yfp, P acrAB -rfp) via time-lapse microscopy, we were able to quantify variability in the marA input and two outputs in both the CRISPRi and wild type systems. We used these fluorescence data to calculate the coordinated diversity in the microcolony by measuring mutual information over time. This metric captures the value of a large spectrum of correlations between two downstream genes without over-valuing high correlations between a few cells, such as would occur during the beginning growth stages of a microcolony  (Tkacik & Walczak 2011). Mirroring the analytical results, we see that there is an advantage to increasing the half-life of MarA, where coordinated phenotypic diversity is higher between the two downstream genes in the CRISPRi strain (Fig.
S3 ). For the other experiments containing CRISPRi knockdown plasmids, cultures were refreshed for 4 hours without addition of inducer. For microscopy movies, we placed cells on 1. 5% MGC low melting point agarose pads  (Dunlop et al. 2008). We used a Nikon Instruments Ti-E microscope to image cells at 100× magnification. Time-lapse movies were taken at a temporal resolution of every three minutes for 10 hours. We used Super Segger cell tracking software to extract fluorescence data from individual cells  (Stylianidou et al. 2016). When computing the coefficient of variation and the mutual information of growing microcolonies of a given size, data from five replicates was combined and binned according to the number of cells in the microcolony (Fig. S2 ). not certified by peer review) is the author/funder.
Additionally, we estimated the diagnostic (early detection) sensitivities of each marker at cut-off points corresponding to 95% and 98% specificity, determined on crude values of the biomarkers and after adjustment for matching factors in our full dataset for all control subjects (N = 213). ROC curves were estimated either for crude marker measurements, without any adjustment, or for risk scores with CA19-9 or ApoA2-ATQ/AT markers as the major discrimination variables, using unconditional logistic regression models that included the matching factors as additional adjustments. Analyses directly based on marker measurements without further adjustments have the advantage that they allow use of pre-established marker cut-points, as used in other studies. As a complementary analysis, the adjusted model estimates account for the fact that the distribution of controls in our matched sample is not representative of the general population, and provide estimates of the general additional discriminative capacity of the markers over the risk factors included for matching  17.
Multivariate models were also used to examine the discrimination capacity of CA19-9 and ApoA2 markers in combination. To test for improvement in discrimination for combined vs. single-marker models the statistical fit of nested models was compared with type-III F-tests within the logistic models. In addition, we calculated the continuous net reclassification improvement (NRI(>0)), which represents the net percent of case and control subjects correctly reclassified as a result of the added marker  18. Internal validation with 1000-fold bootstrapping was applied to adjust the results on discriminative capacity from multivariate models for over-estimation. All analyses were conducted in SAS (version 9. 4, SAS Institute).
0 μg/mL for ApoA2-ATQ/AT), fixing the joint specificity at 98%, the two markers combined yielded sensitivities of 43%, 36% and 7% respectively (Table  3 ), as compared to 36%, 29%, and 5% for CA19-9 alone (Table  2 ). Sensitivity analyses restricting to microscopically confirmed pancreas cancer (N=106) did not reveal any major discrepancies compared to analyses in the full dataset (all pancreas cancer outcomes; N=156) (supplementary Table  S2 ). In multivariable models adjusting for the matching factors as co-variates, and using bootstrapping to correct for possible overfitting, ROC curves (C-statistics) and estimates of SE98 or SE95 were generally comparable to univariate analyses based directly on the marker measurements (see Table  2  and Supplemental Figure  S3 ). Similar to the unadjusted analyses, estimated C-statistics from the adjusted models show on increase in detection discrimination within time windows ≤18 months when the two biomarkers are combined, as compared to either biomarker alone (Supplementary Figure  S3 ).
Acknowledgements Since 1992 the coordination of EPIC is financially supported by the  European Commission (DG-SANCO)  and the  International Agency for Research on Cancer. The national cohorts are supported by  Danish Cancer Society (Denmark) ;  Ligue Contre le Cancer ,  Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale ,  Institut National de la Santé et de la Recherche Médicale (INSERM) (France) ;  German Cancer Aid ,  German Cancer Research Center (DKFZ) ,  Federal Ministry of Education and Research (BMBF) (Germany) ; the  Hellenic Health Foundation (Greece) ;  Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy) ;  Dutch Ministry of Public Health, Welfare and Sports (VWS) ,  Netherlands Cancer Registry (NKR) ,  LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland) ,  World Cancer Research Fund (WCRF) ,  Statistics Netherlands  (The Netherlands);  ERC-2009-AdG 232997  and  Nordforsk, Nordic Centre of Excellence programme on Food, Nutrition and Health (Norway) ;  Health Research Fund (FIS) ,  PI13/00061  to Granada,  PI13/01162  to  EPIC-Murcia, Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, ISCIII RETIC  ( RD06/0020 ) (Spain);  Swedish Cancer Society, Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden) ;  Cancer Research UK  ( 14136  to  EPIC-Norfolk ;  C570/A16491  and  C8221/A19170  to  EPIC-Oxford ),  Medical Research Council  ( 1000143  to  EPIC-Norfolk ,  MR/M012190/1  to  EPIC-Oxford) (United Kingdom ).
3a, b ). The Sema3a + cells at E14. 5 were more restricted to the subendocardial layer of the ventricular myocardium and gave rise to the Cx40 + Purkinje fibres and Cx40 -working cardiomyocytes at P7 and P21 (Fig. 3c, d ). The Sema3a + cells at E18. 5 gave rise to almost all of the Cx40 + Purkinje fibres in the subendocardial surface of the ventricular myocardium at P7 and P21 and gave rise to few if any Cx40 -working cardiomyocytes (Fig. 3e, f ). These data demonstrated that the fate of Sema3a + cardiomyocytes was specialized to Purkinje fibres in the perinatal stage (e. g. , E18. 5) when their cell fate was restricted to the ventricular conduction system (Fig. 3g ). Quantification of the Sema3a + cardiomyocytes. Finally, we quantified the percentage of cardiomyocytes that were labelled with Sema3a-CreERT2; R26-tdTomato in the adult mouse hearts.
The images were acquired with a Zeiss confocal microscope (LSM510), an Olympus confocal microscope (FV1000), and a Zeiss stereo-microscope (AXIO Zoom. V16). To produce the Z-stack confocal images, we scanned 3-4 consecutive XY images on the z-axis with an Olympus confocal microscope (FV1000). Then, the images were analysed with ImageJ (NIH) software. In the stack data, an orthogonal view was used to reveal the merged signals on the XZ and YZ axes. The merged signals and split channels are presented to delineate the signals at a one-cell resolution. Each IHC data is representative of 3-5 individual samples. Whole-mount heart imaging. The whole-mount images of the adult heart were collected with a Zeiss stereo-microscope (AXIO Zoom. V16). The hearts were collected from 8-12-week-old mice, and the blood was removed via stimulation with necropsy tools.
Acknowledgements We thank  Shanghai Biomodel Organism Co. , Ltd. for mouse generation; and  Baojin Wu ,  Guoyuan Chen ,  Zhonghui Weng  and  Aimin Huang  for animal husbandry. We also thank the technical help from  Qiaozhen Liu ,  Wei Bian ,  Tengfei Zhang  and members of  National Center for Protein Science Shanghai  for assistance in microscopy. We thank other members of our laboratory for insightful discussion and technical help throughout this study. This work was supported by the  National key Research & Development Program of China  ( 2016YFC1300600  and  2017YFC1001303 ),  Strategic Priority Research Program  of the  Chinese Academy of Sciences (CAS ,  XDB19000000 ,  XDA16020204 ),  National Science Foundation of China  ( 31730112 ,  91639302 ,  31625019 ,  31571503 ,  31501172 ,  31601168 ,  31701292 ,  91749122 ,  81761138040 ),  Youth Innovation Promotion Association of CAS  ( 2015218 ,  2060299 ),  Key Project of Frontier Sciences of CAS  ( QYZDB-SSW-SMC003 ),  International Cooperation Fund of CAS, National Program for Support of Top-notch Young Professionals  and  Leading Talents ,  Shanghai Science and Technology Commission  ( 17ZR1449600 ,  17ZR1449800 ),  Shanghai Yangfan Project  ( 16YF1413400 ) and  Rising-Star Program  ( 15QA1404300 ),  China Postdoctoral Science Foundation  ( 2016M60337 ,  2016LH0042 ,  2017M621552 ),  China Postdoctoral Innovative Talent Support Program  ( BX201700267 ),  China Young Talents Lift Engineering  ( YESS2016050 ,  2017QNRC001 ),  President Fund of Shanghai Institutes for Biological Sciences (SIBS) ,  Astrazeneca ,  Boehringer Ingelheim ,  Sanofi-SIBS Fellowship ,  Royal Society-Newton Advanced Fellowship, Research Council of Hong Kong  ( 04110515 ,  14111916 ,  C4024-16W ) and  Health and Medical Research Fund  ( 03140346 ,  04152566 ).
In March 2016, the pond was harvested and all survivors (n = 1976) were transferred into indoor tanks for data recording. The same traits as before the overwintering were recorded, BW 2 , SL 2 , FC 2 , and % Fat 2. Furthermore, overwintering performance traits were calculated as follows: i) body weight change during wintering expressed as specific growth rate, SGR 1-2 = (lnw t -lnw 0 )/t -1 Ã 100, where w t is the final body weight (g), w 0 is the initial body weight (g) and t is the duration of growth period in days, ii) absolute fat change, FatCh 1-2 = Fat 2 -Fat 1 , where Fat 2 is the percent fat content after wintering and Fat 1 is the percent fat content before wintering, iii) relative fat change, % FatCh 1-2 = (Fat2 -Fat 1 ) / Fat 1 Ã 100, and iv) survival, during overwintering, Surv 1-2 , with 1 given for survived fish and 0 to fish not found during the trait recording.
This study was supported by the  Ministry of Education, Youth, and Sports of the Czech Republic  (projects  CENAKVA -CZ. 1. 05/2. 1. 00/01. 0024  and  CENAKVA II -LO1205  under the  NPU I program ),  European Union 's  Seventh Framework Programme  ( KBBE. 2013. 1. 2-10 )under grant agreement no. 613611  FISHBOOST (http://www. fishboost. eu/),the  Grant Agency of the University of South Bohemia  (project no. 125/2016/Z ) and project of the  Czech NAAR (NAZV)  no. QK1710310. The projects/grants provided financial support for people working on the study, data collection and analysis but had no role in decision to publish and to prepare the manuscript.
The technique measures the energy transfer from the incident monochromated neutron beam onto the sample due to scattering from diffusing molecules; thus, the faster the molecules diffuse in the sample, the broader the energy distribution of the scattered beam. QENS of gel samples of gelator 1 was measured at two concentrations (0. 3 and 0. 5% w/v) along with the bulk solvent for comparison. Samples were measured at four different, pharmaceutically relevant temperatures (275, 285, 295 and 305 K, see ESI for experimental section). The scattering data clearly show a broader peak for the gel samples at both concentrations than for the bulk solvent. Thus, this model-free comparison of the linewidths from the different samples indicates that the solvent diffuses faster in the gel samples (Figure  1b ).
Acknowledgements We would like to thank  Prof. Jon W. Steed  for support, discussion and supply of gelator 1. The  Institut Laue-Langevin  and ISIS neutron facilities are thanked for the allocation of neutron beamtime (experiments  7-04-80 ,  RB1410058  and  RB1510163 ; these data are permanently curated by the  ILL  and ISIS, respectively, and accessible by these reference numbers). R. M. E. thanks the  Royal Commission for the Exhibition of 1851 , and J. A. F. thanks the  Ramsay Memorial Trust  for a research fellowship.
Geometric and topological properties of protein structures, including surface pockets, interior cavities and cross channels, are of fundamental importance for proteins to carry out their functions. Computed Atlas of Surface Topography of proteins (CASTp) is a web server that provides online services for locating, delineating and measuring these geometric and topological properties of protein structures. It has been widely used since its inception in 2003. In this article, we present the latest version of the web server, CASTp 3. 0. CASTp 3. 0 continues to provide reliable and comprehensive identifications and quantifications of protein topography. In addition, it now provides: (i) imprints of the negative volumes of pockets, cavities and channels, (ii) topographic features of biological assemblies in the Protein Data Bank, (iii) improved visualization of protein structures and pockets, and (iv) more intuitive structural and annotated information, including information of secondary structure, functional sites, variant sites and other annotations of protein residues.
MATERIALS AND METHODS The Computed Atlas of Surface Topography of proteins (CASTp) server uses the alpha shape method (4) developed in computational geometry to identify topographic features, to measure area and volume and to compute imprint  (5) (6) (7) (8). The alpha shape method has also been applied in other studies of cavities and channels in protein structures  (9, 10). The secondary structures are calculated using DSSP  (11). Residue annotations of proteins are obtained from UniProt database  (12)  and mapped to PDB structures with residue-level information from the SIFTS database  (13). The biological assemblies are extracted from the. mmicf files of the PDB database. Only the assemblies with biological significance and designated by the authors of the PDB structures (http://mmcif.
0. All important features of the previous versions of the server are retained, including detecting and characterizing cavities, pockets and channels of protein structures (Figure  1 ). In addition, we have substantially extended its functions by providing pre-computed topographic features of biological assemblies in the PDB database, as well as imprints of negative volumes of these W364 Nucleic Acids Research, 2018, Vol. 46, Web Server issue  topographic features. Furthermore, the user interface has been redesigned, making it more intuitive and informative.
New features of CASTp 3. 0 Pre-computed results for biological assemblies. The atomic coordinates of a PDB ID in the Protein Data Bank describe an asymmetric unit, which is the minimum structure that can produce the unit cell of the crystal through duplication and crystal symmetry operations. For many PDB IDs, the asymmetric unit differs from the biological assembly, which is the unit that has either been shown or is thought to be the functional form of the protein. This difference can be significant, and can make the computation results of a PDB entry biologically irrelevant. For example, PDB ID: 2iwv of the outer membrane porin OmpG contains four units as deposited. Its direct computation leads to the detection of a giant artificial pocket (Figure  2A ), which obscures the actual functional channel (Figure  2B ).
Input The CASTp server takes protein structures in the PDB format and a probe radius as input for topographic computation. Through the intuitive interface, users can either search for pre-computed results using a four-letter PDB ID, or submit their own protein structures to request customized computation. For pre-computed results, a default probe radius of 1. 4 Å is used, which is the standard value for computing solvent accessible surface area. For customized computation request, users can specify any probe radius desired.
Case study Figure  3  illustrates the adenosine triphosphate (ATP) binding pocket on the cyclin-dependent kinase 2 (CDK2, PDB ID: 2xmy) that is automatically identified by the CASTp server. CDK2 is an important enzyme that regulates cell cycle with other CDKs. Many functionally important residues are located in this pocket, including three residues Lys33, Asp86 and Asp145 in the ATP binding site, one residue Asp127 in the catalytic site, two residues Asn132 and Asp145 in Mg binding sites and one residue Lys89 in the binding site that interacts with CDK7, another important cell-cycle-controlling enzyme. Due to the importance of this pocket, it has been widely studied as a target site for antitumor agents  (16). In the CASTp server, users can choose different representations of pockets. For example, the pocket can be displayed as surface (Figure  1 ).
Transmission electron microscopy (TEM) observation The 200-mesh copper grids coated with Formvar-carbon were glow-discharged, and 5-μL samples were directly loaded onto the grids for two minutes before absorbing the liquid excess. The samples were then negatively stained by floating for 30 seconds on a drop of 1. 0% (w/v) sodium silicotungstate. After air drying, images were acquired using a Philips CM120 TEM operated at 80 kV. EV diameter was determined using ImageJ software  [20]. The mean EV density per (sub)species was estimated from the number of vesicles observed in ten 5 μm 2 squares.
Peptides were extracted with 5% formic acid in water/acetonitrile (v/v), dried and suspended in 25μl of 0. 1% formic acid (v/v) and 2% acetonitrile (v/v). LC MS/MS analyses were performed using an Ultimate 3000 RSLC system (Dionex) connected to a LTQ orbitrap mass spectrophotometer (ThermoFisher) by a nanoelectrospray ion source. All MS/MS spectra were searched against Uniprot databases (Mycoplasma (M. ) mycoides subsp. mycoides PG1 T (Uniprot proteome identifier UP000001016, version 2013/10/11, 978 entries), M. agalactiae PG2 T (Uniprot proteome identifier UP000007065, 11/04/2017, 726 entries) and M. fermentans PG18 T (Uniprot proteome identifier UP000006810, 13/05/2017, 1091 entries) by the X!TandemPipeline (open source software developed by PAPPSO, version 3. 4. 3 [22] ). The identified proteins were filtered using E-values <10 -4 for proteins and <0.
01 for peptides with a minimum of 2 peptides. A protein abundance index (PAI) was calculated for each protein in each sample using a relative quantification method, based on spectral counts. PAI, which reflects the relative abundance of the different proteins in each sample, is defined as the number of fragmentation spectra assigned with a significant score divided by the number of observable peptides per protein  [23]. Membrane proteins, i. e. lipoproteins and transmembrane proteins were identified using Uniprot annotations and TMHMM2. 0 prediction  [24]. Other proteins were identified as either cytoplasmic or membrane-bound in reference to functional data but in the absence of characteristic structural features.
Acknowledgments We thank  Lucie Geay  ( UCBL, Lyon, France ) for preparation of samples for TEM and  Sandrine Magnard  ( IBCP, Lyon, France ) for access to ultracentrifugation facilities. The authors declare no conflict of interest. S. G. 's PhD research is jointly funded by the  French Agency for Food, Environmental and Occupational Health and Safety (Anses)  and the  French Agricultural Research Centre for International Development (CIRAD).
2a )  23. Based on this information, we conducted in silico filtering of an in-house, high-chemical diversity structure database of ~3000 compounds for the creation of a focused library of 304 small molecules    2  In vitro screening of a focused library identifies TSL2-interacting small molecules. a Selected scaffolds for the building of a 304-compound, focused RNA-binding chemical library. b Ranking of positive hits (100 μM) from the primary TSL2-interacting screening, with a cut-off value of 20% fluorescence displacement (FD) relative to DMSO (1%) controls (n = 4). Hits NCI377363, PK4C9, BJGF466 and 288D are highlighted. c EC50 binding curves and values of hits NCI377363, PK4C9 and BJGF466 to TSL2 (n = 8; two plates with four replicates each). An EC50 value for 288D could not be calculated due to poor solubility.
Since high-resolution structures of TSL2 have not yet been reported in the nucleotide database (NDB), we first sought to solve the NMR structure of TSL2. A synthetic high-purity, non-labelled TSL2 RNA, as well as an adenine  13  C-labelled TSL2 RNA ( 13 C-A-TSL2), were used to record a large set of NMR spectra that allowed the calculation of a bundle of 40 structures representing the TSL2 ensemble (rootmean-square deviation, RMSD, of 1. 8 Å; Table  1   structures showed A-helical stacking for all base pairs from A1-U19 to A8-U12, capped by a triloop consisting of residues A9-U11, with an unstable loop-closing base pair (A8-U12) that resulted in a temporary pentaloop conformation (see Fig. 6a, b ; below). PK4C9 titration experiments were then performed adding up to 20 equivalents of ligand to the TSL2 RNA.