dataesr/scientific-paragraphs-categorization huggingface dataset:
g. source attribution, antimicrobial resistance, transmission patterns, population structure, etc. ). The WGS data themselves (including a minimum set of descriptive data) should ideally also be publicly available. To fully realise this vision, technical and political challenges must be overcome. PulseNet International will leverage its experience to guide the implementation of genome sequencing in a manner that both meets immediate public health needs and supports broader efforts to preventing and mitigating the effects of food-borne disease worldwide.
Acknowledgments Acknowledgements for manuscript review and feedback:  Aleisha Reimer  ( Public Health Agency of Canada );  Natalie Knox  ( Public Health Agency of Canada );  Jennifer Adams  ( Association of Public Health Laboratories, USA ). The views and opinions of the authors expressed herein do not necessarily state or reflect those of the  Centers for Disease Control and Prevention , the  European Centre for Disease Prevention and Control  or the  European Food Safety Authority. Use of trade names is for identification only and does not imply endorsement by the  Centers for Disease Control and Prevention  or by the  US Department of Health and Human Services.
Network analysis reveals central actors in the grain proteome response to nutrition To provide an integrative view of the grain response to N and S supply and highlight proteins that co-accumulate during grain filling, a directed multilevel network was constructed using proteomic data for the 209 proteins whose abundance was modified by N and/or S supply from 300 to 500°Cd after anthesis. We used a data mining method based on the discovery of association rules between attributes  (Agier et al. , 2007; Vincent et al. , 2015). Associations between proteins and comparisons between treatments were built according to individual protein amounts in the different samples. The resulting network comprised 206 significant linkages (edges) and 111 proteins (nodes), i. e. about 50% of the 209 input proteins, and included the six GSPs, 25 nuclear proteins, and 80 albumin-globulin proteins (Figure  5a ; Tables  S5, S6 ).
Air temperature, relative humidity, and PPF were measured every min and 15-min averages were recorded with a data logger. Thermal time was calculated as cumulative degree-days (0°C base temperature) using average daily air temperature. Until anthesis, each PVC column received 167 mL d -1 of a modified Hoagland's nutrient solution (Castle and Randall, 1987) containing 3 mM N and 0. 1 mM S prepared with demineralized water (1 mM KH 2 PO 4 , 1 mM KNO 3 , 0. 5 mM Ca(NO 3 ) 2 , 0. 5 mM NH 4 NO 3 , 0. 1 mM MgSO 4 , 1. 9 mM MgCl 2 , 3. 5 mM CaCl 2 , 4 mM KCl, 10 µM H 3 BO 3 , 0. 7 µM ZnCl 2 , 0. 4 µM CuCl 2 , 4. 5 µM MnCl 2 , 0. 22 µM MoO 3 and 50 µM EDFS-Fe). At anthesis when N and S demand is low, the nutrient solution was replaced with demineralized water to avoid excess build-up of N and S compounds in plants or potting substrate.
Peptide identification and quantification Nuclear and albumin-globulin proteins were identified by matching peptides against the Uniprot protein database version 2014_07 limited to the Triticum genus using X!Tandem version Sledgehammer 2013. 09. 01. 1 (www. thegpm. org/tandem). Enzymatic cleavage parameters were set as trypsin digestion with one possible missed cleavage. Cys carboxyamidomethylation was set as static modification, whereas Met oxidation, N-terminal deamidation, and N-terminal acetylation were set as variable modifications. Precursor mass tolerance was 10 ppm and fragment mass tolerance was 0. 02. Identified proteins were filtered and grouped using X!TandemPipeline version 3. 3. 4 (http:// pappso. inra. fr/bioinfo/xtandempipeline/). Peptide and protein e-value cut offs were set to 0.
01 and 10 -4 , respectively, with at least two peptides per protein. Relative quantification of all identified peptides was done using MassChroQ software version 2. 1. 3 (Valot et al. , 2011)  and extracting ion chromatograms (XICs) for all identified peptides and integrating the area of XIC peaks at the corresponding retention time. Non repeated peptides were then removed. For the albumin-globulin fraction, these corresponded to peptides present in less than 90% of the samples. For nuclear proteins, this threshold was set at 30%, due to the high effect of the developmental stage on this subproteome, as previously reported on wheat grain  (Bonnot et al. , 2015). To quantify proteins, only peptides.
Accepted Article This article is protected by copyright. All rights reserved. not shared by other proteins and correlated peptides were used. The quantity of the corresponding protein was calculated by summing the quantities of specific peptides for albumin-globulin, and by averaging the quantities of specific peptides for nuclear proteins. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium  (Vizcaíno et al. , 2014)  via the PRIDE partner repository with the dataset identifier PXD006058.
A hierarchical clustering on principal components was computed on proteins with significant nutrition effects using the FactomineR package for R  (Husson et al. , 2015)  and its HCPC function. With this function, a principal component analysis (PCA) was firstly performed on the data set. The clustering was then applied on the result of the PCA, using the Ward's criterion, which is based on the multidimensional variance (i. e. inertia). The number of clusters was determined with the suggested partition, i. e. the one with the higher relative loss of inertia.
For this second semantic, thresholds for support, confidence and lift were set at 0. 3, 0. 9 and 1. 5, respectively. Validated rules were visualized using Cytoscape software version 3. 3. 0 (Smoot et al. , 2011)  and are presented in Table  S6. Comment citer ce document : Bonnot, T. , Bancel, E. , Alvarez, D. , Davanture, M. , Boudet, J. , Pailloux, M. , Zivy, M. , Ravel, C. , Martre, P. (  2017 ). Grain subproteome responses to nitrogen and sulfur supply in diploid wheat Triticum monococcum ssp. monococcum. Plant Journal, 91 (5), 894-910. DOI : 10. 1111/tpj. 13615.
ACKNOWLEDGEMENTS We thank  Richard Blanc  ( UMR GDEC, INRA, Blaise Pascal University ) for help in the greenhouse and  Mireille Dardevet ,  Annie Faye ,  Marielle Merlino ,  Sibille Perrochon  and  Isabelle Nadaud  for help collecting grains (UMR GDEC). This work was supported by a Ph. D. grant from the  French Ministry for Higher Education and Research to TB  and funding from the  French Government  managed by the  National Research Agency (ANR)  in the framework of Investments for the  Future  ( ANR-10-BTBR-03 ),  France AgriMer  and the  French Fund Supporting Plant Breeding (FSOV). The authors declare no conflict of interest.
Abstract The Concise Guide to PHARMACOLOGY 2017/18 is the third in this series of biennial publications. This version provides concise overviews of the key properties of nearly 1800 human drug targets with an emphasis on selective pharmacology (where available), plus links to an open access knowledgebase of drug targets and their ligands (www. guidetopharmacology. org), which provides more detailed views of target and ligand properties. Although the Concise Guide represents approximately 400 pages, the material presented is substantially reduced compared to information and links presented on the website. It provides a permanent, citable, point-in-time record that will survive database updates. The full contents of this section can be found at http://onlinelibrary. wiley. com/doi/10. 1111/bph.
13882/full. In addition to this overview, in which are identified 'Other protein targets' which fall outside of the subsequent categorisation, there are eight areas of focus: G protein-coupled receptors, ligand-gated ion channels, voltage-gated ion channels, other ion channels, nuclear hormone receptors, catalytic receptors, enzymes and transporters. These are presented with nomenclature guidance and summary information on the best available pharmacological tools, alongside key references and suggestions for further reading. The landscape format of the Concise Guide is designed to facilitate comparison of related targets from material contemporary to mid-2017, and supersedes data presented in the 2015/16 and 2013/14 Concise Guides and previous Guides to Receptors and Channels. It is produced in close conjunction with the Nomenclature Committee of the Union of Basic and Clinical Pharmacology (NC-IUPHAR), therefore, providing official IUPHAR classification and nomenclature for human drug targets, where appropriate.
Introduction In order to allow clarity and consistency in pharmacology, there is a need for a comprehensive organisation and presentation of the targets of drugs. This is the philosophy of the IUPHAR/BPS Guide to PHARMACOLOGY presented on the online free access database (http://www. guidetopharmacology. org/). This database is supported by the British Pharmacological Society (BPS), the International Union of Basic and Clinical Pharmacology (IUPHAR), the University of Edinburgh and previously the Wellcome Trust. Data included in the Guide to PHARMACOLOGY are derived in large part from interactions with the subcommittees of the Nomenclature Committee of the International Union of Basic and Clinical Pharmacology (NC-IUPHAR). A major influence on the development of the database was Tony Harmar (1951-2014), who worked with a passion to establish the curators as a team of highly informed and informative individuals, with a focus on high-quality data input, ensuring a suitably validated dataset.
The Editors of the Concise Guide have compiled the individual records, in concert with the team of Curators, drawing on the expert knowledge of these latter subcommittees. The tables allow an indication of the status of the nomenclature for the group of targets listed, usually previously published in Pharmacological Reviews. In the absence of an established subcommittee, advice from several prominent, independent experts has generally been obtained to produce an authoritative consensus on nomenclature, which attempts to fit in within the general guidelines from NC-IUPHAR. This current edition, the Concise Guide to PHARMACOLOGY 2017/18, is the latest snapshot of the database in print form, following on from the Concise Guide to PHARMACOLOGY 2015/16. It contains data drawn from the online database as a rapid overview of the major pharmacological targets.
Thus, there are many fewer targets presented in the Concise Guide compared to the online database. The priority for inclusion in the Concise Guide is the presence of quantitative pharmacological data. This means that often orphan family members are not presented in the Concise Guide, although structural information is available on the online database. The organisation of the data is tabular (where appropriate) with a standardised format, where possible on a single page, intended to aid understanding of, and comparison within, a particular target group. The Concise Guide is intended as an initial resource, with links to additional reviews and resources for greater depth and information. Pharmacological and structural data focus primarily on human gene products, wherever possible, with links to HGNC gene nomenclature and UniProt IDs.
In a few cases, where data from human proteins are limited, data from other species are indicated. Pharmacological tools listed are prioritised on the basis of selectivity and availability. That is, agents (agonists, antagonists, inhibitors, activators, etc. ) are included where they are both available (by donation or from commercial sources, now or in the near future) AND the most selective. The Concise Guide is divided into nine sections, which comprise pharmacological targets of similar structure/function. These are G protein-coupled receptors, ligand-gated ion channels, voltage-gated ion channels, other ion channels, catalytic receptors, nuclear hormone receptors, enzymes, transporters and other protein targets. We hope that the Concise Guide will provide for researchers, teachers and students a state-of-the art source of accurate, curated information on the background to their work that they will use in the Introductions to their Research Papers or Reviews, or in supporting their teaching and studies.
Neutral amino acid transporter subfamily S390 SLC8 family of sodium/calcium exchangers S390 SLC9 family of sodium/hydrogen exchangers S391 SLC10 family of sodium-bile acid co-transporters S392 SLC11 family of proton-coupled metal ion transporters S393 SLC12 family of cation-coupled chloride transporters S395 SLC13 family of sodium-dependent sulphate/carboxylate transporters S395 SLC14 family of facilitative urea transporters S396 SLC15 family of peptide transporters S398 SLC16 family of monocarboxylate transporters S399 SLC17 phosphate and organic anion transporter family S399 Type I sodium-phosphate co-transporters S400 Sialic acid transporter S400 Vesicular glutamate transporters (VGLUTs) S401 Vesicular nucleotide transporter S401 SLC18 family of vesicular amine transporters S403 SLC19 family of vitamin transporters S403 SLC20 family of sodium-dependent phosphate transporters S404 SLC22 family of organic cation and anion transporters S404 Organic cation transporters (OCT) S405 Organic zwitterions/cation transporters (OCTN) S406 Organic anion transporters (OATs) S407 Urate transporter S407 SLC23 family of ascorbic acid transporters S409 SLC24 family of sodium/potassium/calcium exchangers S409 SLC25 family of mitochondrial transporters S410 Mitochondrial di-and tri-carboxylic acid transporter subfamily S411 Mitochondrial amino acid transporter subfamily S412 Mitochondrial phosphate transporters S412 Mitochondrial nucleotide transporter subfamily S413 Mitochondrial uncoupling proteins S414 Miscellaneous SLC25 mitochondrial transporters S414 SLC26 family of anion exchangers S415 Selective sulphate transporters S415 Chloride/bicarbonate exchangers S416 Anion channels S416 Other SLC26 anion exchangers S417 SLC27 family of fatty acid transporters S418 SLC28 and SLC29 families of nucleoside transporters S418 SLC28 family S419 SLC29 family S420 SLC30 zinc transporter family S421 SLC31 family of copper transporters S422 SLC32 vesicular inhibitory amino acid transporter S422 SLC33 acetylCoA transporter S423 SLC34 family of sodium phosphate co-transporters S424 SLC35 family of nucleotide sugar transporters S425 SLC36 family of proton-coupled amino acid transporters S426 SLC37 family of phosphosugar/phosphate exchangers S427 SLC38 family of sodium-dependent neutral amino acid transporters S427 System A-like transporters S428 System N-like transporters S428 Orphan SLC38 transporters S429 SLC39 family of metal ion transporters S430 SLC40 iron transporter S430 SLC41 family of divalent cation transporters S431 SLC42 family of Rhesus glycoprotein ammonium transporters S432 SLC43 family of large neutral amino acid transporters S433 SLC44 choline transporter-like family S433 SLC45 family of putative sugar transporters S434 SLC46 family of folate transporters S435 SLC47 family of multidrug and toxin extrusion transporters S436 SLC48 heme transporter S436 SLC49 family of FLVCR-related heme transporters S437 SLC50 sugar transporter S438 SLC51 family of steroid-derived molecule transporters S438 SLC52 family of riboflavin transporters S439 SLCO family of organic anion transporting polypeptides S442 Patched family Searchable database: http://www.
Acknowledgements We are extremely grateful to the  British Pharmacological Society  and the  International Union of Basic and Clinical Pharmacology , for financial support of the website and for advice from the  NC-IUPHAR  subcommittees. We thank the  University of Edinburgh , who host the www. guidetopharmacology. org website. Previously, the  International Union of Basic and Clinical Pharmacology  and the  Wellcome Trust  ( 099156/Z/12/Z ]) also supported the initiation and expansion of the database. We are also tremendously grateful to the long list of collaborators from NC-IUPHAR subcommittees and beyond, who have assisted in the construction of the Concise Guide to PHARMACOLOGY 2017/18 and the online database www. GuideToPHARMACOLOGY. org. Further, we wish to thank  Toni Wigglesworth  for her assistance in the co-ordination of correspondence with these collaborators.
S389 Neutral amino acid transporter subfamily S390 SLC8 family of sodium/calcium exchangers S390 SLC9 family of sodium/hydrogen exchangers S391 SLC10 family of sodium-bile acid co-transporters S392 SLC11 family of proton-coupled metal ion transporters S393 SLC12 family of cation-coupled chloride transporters S395 SLC13 family of sodium-dependent sulphate/carboxylate transporters S395 SLC14 family of facilitative urea transporters S396 SLC15 family of peptide transporters S398 SLC16 family of monocarboxylate transporters S399 SLC17 phosphate and organic anion transporter family S399 Type I sodium-phosphate co-transporters S400 Sialic acid transporter S400 Vesicular glutamate transporters (VGLUTs) S401 Vesicular nucleotide transporter S401 SLC18 family of vesicular amine transporters S403 SLC19 family of vitamin transporters S403 SLC20 family of sodium-dependent phosphate transporters S404 SLC22 family of organic cation and anion transporters S404 Organic cation transporters (OCT) S405 Organic zwitterions/cation transporters (OCTN) S406 Organic anion transporters (OATs) S407 Urate transporter S407 SLC23 family of ascorbic acid transporters S409 SLC24 family of sodium/potassium/calcium exchangers S409 SLC25 family of mitochondrial transporters S410 Mitochondrial di-and tri-carboxylic acid transporter subfamily S411 Mitochondrial amino acid transporter subfamily S412 Mitochondrial phosphate transporters S412 Mitochondrial nucleotide transporter subfamily S413 Mitochondrial uncoupling proteins S414 Miscellaneous SLC25 mitochondrial transporters S414 SLC26 family of anion exchangers S415 Selective sulphate transporters S415 Chloride/bicarbonate exchangers S416 Anion channels S416 Other SLC26 anion exchangers S417 SLC27 family of fatty acid transporters S418 SLC28 and SLC29 families of nucleoside transporters S418 SLC28 family S419 SLC29 family S420 SLC30 zinc transporter family S421 SLC31 family of copper transporters S422 SLC32 vesicular inhibitory amino acid transporter S422 SLC33 acetylCoA transporter S423 SLC34 family of sodium phosphate co-transporters S424 SLC35 family of nucleotide sugar transporters S425 SLC36 family of proton-coupled amino acid transporters S426 SLC37 family of phosphosugar/phosphate exchangers S427 SLC38 family of sodium-dependent neutral amino acid transporters S427 System A-like transporters S428 System N-like transporters S428 Orphan SLC38 transporters S429 SLC39 family of metal ion transporters S430 SLC40 iron transporter S430 SLC41 family of divalent cation transporters S431 SLC42 family of Rhesus glycoprotein ammonium transporters S432 SLC43 family of large neutral amino acid transporters S433 SLC44 choline transporter-like family S433 SLC45 family of putative sugar transporters S434 SLC46 family of folate transporters S435 SLC47 family of multidrug and toxin extrusion transporters S436 SLC48 heme transporter S436 SLC49 family of FLVCR-related heme transporters S437 SLC50 sugar transporter S438 SLC51 family of steroid-derived molecule transporters S438 SLC52 family of riboflavin transporters S439 SLCO family of organic anion transporting polypeptides S442 Patched family Searchable database: http://www.
Searchable database: http://www. guidetopharmacology. org/index. jsp Overview S3 Full Contents of ConciseGuide: http://onlinelibrary. wiley. com/doi/10. 1111/bph. 13882/full S365 ABCC subfamily S287 M28: Aminopeptidase Y S324 2-Acylglycerol ester turnover S366 ABCD subfamily of peroxisomal ABC transporters S287 M19: Membrane dipeptidase S325 Eicosanoid turnover S367 ABCG subfamily S288 S1: Chymotrypsin S325 Cyclooxygenase S368 F-type and V-type ATPases S288 T1: Proteasome S326 Prostaglandin synthases S368 F-type ATPase S289 S8: Subtilisin S327 Lipoxygenases S368 V-type ATPase S289 S9: Prolyl oligopeptidase S328 Leukotriene and lipoxin metabolism S369 P-type ATPases S290 Acetylcholine turnover S329 GABA turnover S369 Na + /K + -ATPases S291 Adenosine turnover S331 Glycerophospholipid turnover S369 Ca 2+ -ATPases S292 Amino acid hydroxylases S331 Phosphoinositide-specific phospholipase C S370 H + /K + -ATPases S293 L-Arginine turnover S332 Phospholipase A2 S370 Cu + -ATPases S294 2.
(2016) Adiponectin signaling and function in insulin target tissues. J Mol Cell Biol 8: 101-9 [PMID:26993044] Adipo1 receptor Adipo2 receptor HGNC, UniProt ADIPOR1, Q96A54 ADIPOR2, Q86V24 Rank order of potency globular adiponectin (ADIPOQ, Q15848) > adiponectin (ADIPOQ, Q15848) globular adiponectin (ADIPOQ, Q15848) = adiponectin (ADIPOQ, Q15848) Comments: T-Cadherin (CDH13, P55290) has also been suggested to be a receptor for (hexameric) adiponectin [33]. Searchable database: http://www. guidetopharmacology. org/index.
reading on Blood coagulation components Astermark J. (2015) FVIII inhibitors: pathogenesis and avoidance. Blood 125: 2045-51 [PMID:25712994] Girolami A et al. (2017) New clotting disorders that cast new light on blood coagulation and may play a role in clinical practice. J Thromb Thrombolysis 44: 71-75 [PMID:28251495] Rana K et al. (2016) Blood flow and mass transfer regulation of coagulation. Blood Rev 30: 357-68 [PMID:27133256] Searchable database: http://www. guidetopharmacology. org/index.
jsp Carrier proteins S8 Full Contents of ConciseGuide: http://onlinelibrary. wiley. com/doi/10. 1111/bph. 13882/full Further reading on Carrier proteins Alshehri B et al. (2015) The diversity of mechanisms influenced by transthyretin in neuro- Galant NJ et al. (2017) Transthyretin amyloidosis: an under-recognized neuropathy and cardiomy- biology: development, disease and endocrine disruption. J Neuroendocrinol 27: 303-23 opathy. Clin Sci (Lond) 131: 395-409 [PMID:28213611] [PMID:25737004] Delliere S et al. (2017) Is transthyretin a good marker of nutritional status? Clin Nutr 36: 364-370 [PMID:27381508] Nomenclature transthyretin HGNC, UniProt TTR, P02766 Common abreviation TTR Searchable database: http://www. guidetopharmacology. org/index.
jsp Tubulins S15 Full Contents of ConciseGuide: http://onlinelibrary. wiley. com/doi/10. 1111/bph. 13882/full [13]), vincristine, eribulin (pIC 50 8. 2)[58], paclitaxel (pEC 50 8. 1)[61], colchicine (pIC 50 8)[13], cabazitaxel, docetaxel, ixabepilone tubulin tubulin tubulin beta class I tubulin beta 3 tubulin beta 4B tubulin beta 8 alpha 1a alpha 4a class III class IVb class VIII HGNC, TUBA1A, TUBA4A, TUBB, P07437 TUBB3, Q13509 TUBB4B, P68371 TUBB8, Q3ZCM7 UniProt Q71U36 P68366 Inhibitors - - vinblastine (pIC combretastatin A4 - - (pIC 50 8. 2) [22] Further reading on Tubulins Gadadhar S et al. (2017) The tubulin code at a glance. J Cell Sci 130: 1347-1353 [PMID:28325758] Penna LS et al. (2017) Anti-mitotic agents: Are they emerging molecules for cancer treatment?Pharmacol Ther 173: 67-82 [PMID:28174095]Searchable database: http://www.
Contradictions with Network Analysis Methodologically, the question raised is how to assess this emergent property. In the articles that advocate the Network Analysis approach in psychometrics, the complex system is assumed to be characterized by a simulated network based on empirical data. Concretely, "human cognition is simulated with a network of several interrelated nodes"  (Dalege et al. , 2016, p. 3). The nodes are the components (or symptoms) of the psychological attribute under study. This model "deal[s] with complex systems, " and at the same time can be "simulated" using empirical data  (Dalege et al. , 2016, p. 2). Consequently, for these authors, network is "a realistic conceptualization of empirical data on attitudes"  (Dalege et al. , 2016, p. 16)  and "empirical data would be to use empirically estimated attitude networks as input for data simulation"  (Dalege et al.