dataesr/scientific-paragraphs-categorization huggingface dataset:
Optimization procedure and statistical analyses In order to obtain vertical particle fluxes at a fine spatial scale during the field campaign, we computed a regional algorithm linking the particle size distributions recorded by the UVP5 (0. 08-4. 2 mm ESD) to mass and POC fluxes estimated by the sediment traps at overlapping sampling locations and periods. When more than one UVP5 deployment corresponded to only one sediment trap sample (mainly for long-term traps; see Fig. A1a ), the abundance of particles for each size class for all corresponding profiles were averaged. A sensitivity test to document the degree of confidence in our approach was also conducted using a multiple random resampling of our database (Appendix A, Fig. A1b ). The identifiable zooplankton dataset (≥ 0. 6 mm ESD) was removed from the UVP5 particle database before proceeding to the computation of the regional algorithm.
The optimization procedure of  Guidi et al. (2008)  making use of the Matlab function fminsearch (MathWorks, USA) was adapted to our dataset to find the A and b values that minimized the log-transformed differences between sediment trap data (mass and POC) and the spectrally estimated mass or POC fluxes in the following power-law equation: F = 4. 2 0. 08 n • A • d b , ( 1 ) where F is the flux integrated from 0. 08-4. 2 mm ESD (mass or POC, in mg m -2 d -1 , n is the concentration of particles in a given size class (# L -1 , or # 10 -3 m -3 ), A is a constant (mg m 1-b d -1 ), d is the mean ESD of particles in a given size class (mm, or 10 -3 m), and b is the scaling exponent of the power-law relationship (no unit). On the basis of the scaling exponent b, the mean fractal dimension (D, unitless) of particles (e.
g. ,  Jackson and Burd, 1998; Li and Logan, 2000; Guidi et al. , 2008)  along the size spectrum can be easily calculated using the equation D = b + 1 2. ( 2 ) Statistical analyses to evaluate the influence of spatial patterns, linear trend, and physical-biological variables on vertical POC fluxes were conducted following the approach of  Borcard et al. (2011). We adapted this step-by-step methodology by making use of redundancy analyses (RDAs) between station locations, POC flux size classes (i. e. , used as "species") integrated over the top 200 m (i. e. , the water interval corresponding to the sampling conducted with sediment traps, Table  1 ), and potential explanatory variables available at the same spatial and temporal resolution than the UVP5 dataset (including a set of spatial structures; see below).
Prior to analysis, POC fluxes were log-transformed to give equal weights to all size classes. All analyses were conducted using the R freeware (http://www. r-project. org/) with the appropriate packages  (Borcard et al. , 2011). Spatial structures of POC fluxes across the Mackenzie Shelf were obtained using the principal coordinates of neighbor matrices (PCNM) procedure. The goal of the PCNM method is to identify patterns and gradients across a whole range of fine-to-broad scales (2-D) perceptible within a given dataset  (Borcard et al. , 2004). This procedure generates a suite of variables that can readily be used in further analyses, such as RDAs and variation partitioning (e. g. ,  Peres-Neto et al. , 2006). When used on an irregular sampling designsuch as in the present analysis -the PCNM functions correspond to irregular sinusoidal-like waveforms, within which spatial structures along the X-Y geographic coordinates are repeated.
Vertical particle flux dynamics as obtained from the UVP5 dataset We obtained 21 overlaps between sediment trap sampling and UVP5 deployments over the course of the field campaign in July-August. The optimization procedure generated power-law parameters (Table  2 ) that transformed particles within the size range of 0. 08-4. 2 mm ESD into vertical mass and POC flux estimates with relatively strong coefficients of determination (r 2 = 0. 73 for DW fluxes; r 2 = 0. 68 for POC fluxes; Fig. 8 ). A multiple random resampling exercise of our database (Appendix A, Fig. A1b ) enabled us to estimate a standard deviation corresponding to ∼ 20 % up to ∼ 33 % of the various A and b parameters (Table  2 ), which is in the range of comparable previous studies. The scaling exponent b of the vertical mass flux algorithm yielded a low fractal dimension (i.
N/A: not available. When applied to the whole UVP5 dataset, vertical POC fluxes across the Mackenzie Shelf region can be conveniently plotted for a 3-D domain (Fig. 9 ). This enables quick grasping of the main vertical flux structures, patterns and gradients. For an in-depth description on how Fig. 9  was made and a further 3-D interactive visualization of POC fluxes across the study region using geographic information system (EnterVol for ArcGIS, C Tech, USA), see Appendix C and the online Supplement. The 7 Malina sections mapped in Fig. 1  are illustrated as 2-D cross-shelf vertical planes in Fig. 9 , whereas ArcticNet stations located on the middle shelf and other Malina stations correspond to the small alongshelf planes. Vertical POC fluxes >75 mg C m -2 d -1 were restricted to the shelf environment (<100 m isobath) and to the benthic boundary layer on the slope, in particular in Kugmallit Valley and close to the Mackenzie Canyon (Fig.
9 ). At some locations on the inner shelf and in the Mackenzie Canyon, vertical POC fluxes, as estimated with our empirical relationships, were remarkably high -such as at shallow stations 380/390 and 680/690 (Fig. 1 ), where POC fluxes ranged from ∼ 1 up to ∼ 5 g C m -2 d -1. Relatively high POC fluxes (>75 mg C m -2 d -1 ) were also detected at the beginning of the cruise in July when compared with the whole dataset, and especially with data obtained for the third week of August when fluxes were overall very low (e. g. , line 400, stations 235 and 135; also short-term trap deployments as seen in Fig. 7 ). Around Cape Bathurst, part of the high POC fluxes on the shelf appeared to feed a lateral (i. e. , oblique) export of POC toward the deeper layers (Fig. 9 ). At the mouth of Amundsen Gulf, the abrupt transition from a high-to-low POC flux regime was linked to a real shift in the particle abundance from Cape Bathurst to Banks Island -and not to an artifact of the visualization software.
Ecosystem function and variation partitioning of the spatial-temporal trends and abiotic-biotic control of POC fluxes Investigation of ecosystem variability from a vertical flux perspective is a fruitful approach to get insights on pelagic food web structure and function (e. g. ,  Wassmann, 1998; Reigstad et al. , 2008; Forest et al. , 2010b). Across the Mackenzie Shelf, ecosystem functioning was studied with the combination of sediment traps and ancillary data in several publications to date (e. g. ,  O'Brien et al. , 2006; Forest et al. , 2008; Juul-Pedersen et al. , 2010; Sampei et al. , 2011). However, none of these studies had a multiparameter dataset with a spatial resolution fine enough to proceed to a "state-of-the-art" variation partitioning analysis of vertical flux predictors, corollaries, and spatial patterns.
e. , positive Moran's I, across a range of broad-to-fine scales). Maps of (h-i) the fitted scores of the first two canonical axes from the redundancy analysis (representing 44. 5 % and 5. 0 % of the total PCNM variance) performed between the POC flux size classes, station locations, and the significant PCNM functions (Fig. 14b ). The station locations across the maps correspond to their geographical position as illustrated in Fig. 1. The scale of every PCNM function is zero-centered and corresponds to the distance (km) along an irregular sinusoidal-like waveform function describing the repetition of broad-to-fine scale spatial structures along the X-Y geographic coordinates. The PCNM value of each station (i. e. , positive and negative values) corresponds to the fitted value of each POC flux dataset according to these PCNM waveform functions.
A convenient way to demonstrate that, even if low, the vertical particle fluxes recorded in our study represent simply the lower tail of the flux distribution across Arctic shelves is actually to present these results in the context of the particulate matter inventory observed at the sampling stations (Fig. A1a ). Using the UVP5 dataset, we have detected a minuscule pool of particulate matter at some of the sediment trap locations (down to only ∼ 3 particles per liter in total), which also corresponded generally to very low mass fluxes (<60 mg DW m -2 d -1 ). Conversely, high mass fluxes (up to 1000 mg DW m -2 d -1 ) were detected at stations where a total particle inventory ranging from 10 to 100 particles per liter was detected. This observed gradient set the stage for the development of an empirical equation linking the inventory of particles across the various size classes (0.
08-4. 2 mm ESD) to the vertical fluxes as recorded by the sediment traps. We have used a log-log relationship to estimate vertical fluxes using the UVP5 dataset in order to keep coherency with previous studies (Table  2 ) and to give equal weight to low and high fluxes during the minimization procedure. Since this computation (Sect. 2. 6) yielded only one solution for the A and b parameters, we also conducted a sensitivity analysis using a multiple random resampling of   2 ). our database (n = 1000, using arbitrarily from 5 to 20 data points each time) in order to provide a standard deviation for these two parameters (Table  2 ). Result of this random permutation exercise (Fig. A1b ) showed that the zone of the most probable parameters was actually centered on the parameters obtained using the full database (see Sect.
based on Chl-a concentration (mg Chl a m -3 ), beam attenuation coefficient (c p , m -1 ) and water temperature (T , °C). These data were averaged over a 5 m interval centered on the depth of every in situ BP rate (expressed in pmol Leu L -1 h -1 ). Here Chl a and c p were used as combined proxies for organic carbon availability from autochthonous and allochthonous matter sources. The multiple linear regression provided the following equation: Modeled and measured BP data were converted to carbon using a conversion factor 1. 2 kg C per mole of leucine, which provided a fit with a slope of 0. 86 (Fig. A3 ). Supplementary material related to this article is available online at: http://www. biogeosciences. net/10/ 2833/2013/bg-10-2833-2013-supplement. zip.
Acknowledgements. This work would not have been possible without the professional and enthusiastic assistance of the officers and crew members of the  CCGS Amundsen. We express gratitude to  L. Prieur  and  C. Marec  for their help in the deployment of the CTD rosette and for the onboard processing of UVP5 data. We thank  J. Martin ,  J. Gagnon ,  A. Mignot , and  M. Gosselin  for sharing the chlorophyll data in order to post-calibrate the fluorometer. We thank  P. Guillot  for the validation of physical data. We thank  M. Fortier ,  K. Lévesque , and  J. Ehn  for the organization of the fieldwork and workshops and for support at sea. We are grateful to three anonymous reviewers for insightful comments that improved the initial manuscript. This study was conducted as part of the  Malina Scientific Program  funded by  ANR (Agence nationale de la recherche) ,  INSU-CNRS (Institut national des sciences de l'univers -Centre national de la recherche scientifique) ,  CNES  ( Centre national d'études spatiales)  and  ESA  ( European Space Agency).
A. Forest et al. : Vertical flux variability and forcings in Beaufort Sea Variation partitioning analysis of the vertical POC fluxes derived from the UVP5 dataset (154 stations, 17 size classes, all integrated over the top 200 m of the water column) as based on redundancy analyses of three sets of explanatory variables (Fig. 100% Linear trend Spatial trend due to sampling linked to PCNM date & location eigenfunctions 4. 7% 8. 2% 6. 7% 27. 8% 10. 6% -1. 5% 13. 0% Trend induced by biological & environmental variables Residuals = 30. 5% Fig. 16.
The International Union of Basic and Clinical Pharmacology/British Pharmacological Society (IUPHAR/BPS) Guide to PHARMACOLOGY (http:// www. guidetopharmacology. org) is a new open access resource providing pharmacological, chemical, genetic, functional and pathophysiological data on the targets of approved and experimental drugs. Created under the auspices of the IUPHAR and the BPS, the portal provides concise, peer-reviewed overviews of the key properties of a wide range of established and potential drug targets, with in-depth information for a subset of important targets. The resource is the result of curation and integration of data from the IUPHAR Database (IUPHAR-DB) and the published BPS 'Guide to Receptors and Channels' (GRAC) compendium. The data are derived from a global network of expert contributors, and the information is extensively linked to relevant databases, including ChEMBL, DrugBank, Ensembl, PubChem, UniProt and PubMed.
INTRODUCTION Online resources have become indispensable tools for pharmacology and drug discovery, in common with other disciplines in the biomedical sciences. Databases such as ChEMBL  (1)  and PubChem (2) provide extensive information on the bioactivity and chemical structures of approved and experimental drugs and their interaction with targets, either manually curated from the medicinal chemistry literature (ChEMBL) or uploaded by depositors (PubChem). To complement these large-scale resources, there is a need for an in-depth, expert-curated overview of the key targets and ligands, to foster basic and clinical research and innovative drug discovery, and to educate the next generation of researchers. The International Union of Basic and Clinical Pharmacology/British Pharmacological Society (IUPHAR/BPS) Guide to PHARMACOLOGY portal (http://www.
guidetopharma cology. org) is being developed to assist research in pharmacology, drug discovery and chemical biology in academia and industry, by providing: (i) an authoritative synopsis of the complete landscape of current and research drug targets; (ii) an accurate source of information on the basic science underlying drug action; (iii) guidance to researchers in selecting appropriate compounds for in vitro and in vivo experiments, including commercially available pharmacological tools for each target; and (iv) an integrated educational resource for researchers, students and the interested public. The Guide to PHARMACOLOGY portal has been online since December 2011. The current release of the database (October 2013) integrates two well-established sources. The first of these is the IUPHAR Database [IUPHAR-DB:  (3) ], which provides in-depth, integrative views of the pharmacology, genetics, functions and pathophysiology of important target families, including G protein-coupled receptors (GPCRs), ion channels and nuclear hormone receptors (NHRs).
The second is the BPS 'Guide to Receptors and Channels' [GRAC:  (4) ], a compendium, previously published in print, providing concise overviews of the key properties of a wider range of targets than those covered in IUPHAR-DB, together with their endogenous ligands, experimental drugs, radiolabelled ligands and probe compounds, with recommended reading lists for newcomers to each field. Management and peer review of the new resource is the responsibility of the IUPHAR Committee on Receptor Nomenclature and Drug Classification (NC-IUPHAR), which acts as the scientific advisory and editorial board. The organization has an international network of over 700 expert volunteers organized into 60 subcommittees dealing with individual target families. The subcommittee members contribute expertize in several ways, including identifying the key pharmacological properties of each target, along with quantitative activity data from the research literature.
NC-IUPHAR also directly supports the Guide to PHARMACOLOGY through its work in monitoring 'deorphanization' of receptors (i. e. identifying new endogenous ligands), revising receptor nomenclature in collaboration with HUGO Gene Nomenclature Committee (HGNC) database (5-7), liaising with journals, and developing standards and terminology in quantitative pharmacology  (8) (9) (10). The primary sources of data in the Guide to PHARMACOLOGY are distinct from the medicinal chemistry and natural product literature extracted by ChEMBL. Our focus is on data and contextual information relevant to the preclinical phases of drug discovery and includes extensive quantitative and chemical information manually curated from the primary research literature, predominantly from the leading non-specialist scientific journals and widely read specialist journals (Figure  1 ).
CONTENT AND DATA CURATION The current version of the database includes pharmacologically relevant data and information on 2485 human targets including GPCRs, ion channels, NHRs, catalytic (enzyme linked) receptors, transporters and enzymes (including all protein kinases) (Table  1 ). Also included, is information on the genetics, emerging pharmacology, functions and pathophysiology of 130 orphan GPCRs  (7). Presently, the resource describes the interactions between target proteins and 6064 distinct ligand entities (Table  1 ). Ligands are listed against targets by their action (e. g. activator, inhibitor), and also classified according to substance types and their status as approved drugs. Classes include metabolites (a general category for all biogenic, non-peptide, organic molecules including lipids, hormones and neurotransmitters), synthetic organic chemicals (e.
g. small molecule drugs), natural products, mammalian endogenous peptides, synthetic and other peptides including toxins from non-mammalian organisms, antibodies, inorganic substances and other, not readily classifiable compounds. The new database was constructed by integrating data from IUPHAR-DB (3) and the published GRAC compendium (  4 ). An overview of the curation process is depicted as an organizational flow chart in Figure  2. New information was added to the existing relational database behind IUPHAR-DB and new webpages were created to display the integrated information. For each new target, information on human, mouse and rat genes and proteins, including gene symbol, full name, location, gene ID, UniProt and Ensembl IDs was manually curated from HGNC (5), the Mouse Genome Database (MGD) at Mouse Genome Informatics (MGI)  (11) , the Rat Genome Database (RGD)  (12) , UniProt  (13)  and Ensembl  (14) , respectively.
In addition, 'Other names', target-specific fields such as 'Principal transduction', text from the 'Overview' and 'Comments' sections and reference citations (downloaded from PubMed; http://www. ncbi. nlm. nih. gov/pubmed) were captured from GRAC and uploaded into the database against a unique Object ID. For targets present in both IUPHAR-DB and GRAC, entries were cross-checked and merged. A representative target family page is shown in Figure  3. For the integration exercise, all ligands listed in GRAC were first checked against IUPHAR-DB using name-, synonym-and structure-based comparisons. For over 1000 ligands, there was an existing IUPHAR-DB entry that matched. The remaining new ligands (1900) were curated using the workflow already established for the population of IUPHAR-DB with ligand structures  (15).
An overview of the process is outlined below. Interrogation of multiple databases and direct literature checks captured the correct structural information, nomenclature and target mapping for each ligand. All small molecules were resolved against a PubChem Compound Identifier (CID) as a primary molecular identifier and representative chemical structure  (2). Each ligand was then uploaded into the resource with a unique ID. The quantitative pharmacological activity data of each ligand was captured from GRAC and uploaded. Ligands have individual pages (Figure  3 ) providing 2D chemical structures or peptide sequences, calculated physico-chemical properties, classification and approval status for human clinical use, the International Union of Pure and Applied Chemistry (IUPAC) name and other names used as synonyms.
International Nonproprietary Names (INNs) are also currently provided for 730 compounds. INNs are the official non-proprietary or generic names given to pharmaceutical substances, as designated by the World Health Organization (WHO; http://www. who. int/medicines/services/inn/en/). For small molecules, simplified molecular input line entry specification (SMILES), the IUPAC International Chemical Identifiers (InChI string and InChIKey) and Chemical Abstracts Service (CAS) registry numbers (http://www. cas. org/ index. html) are provided. Peptides are specified by oneand three-letter amino acid sequences, any post-translational modifications and details of their protein precursors. Links are provided to corresponding entries in relevant bioactivity and chemistry resources including BindingDB  (16) , Chemical Entities of Biological Interest (ChEBI)  (17) , ChEMBL (1), ChemSpider  (18) , DrugBank  (19) , Human Metabolome Database (HMDB)  (20) , PharmGKB  (21) , RCSB Protein Data Bank  (22) , UniProt  (13)  and ZINC  (23).
Ligand pages also display a list of structurally similar ligands and a summary of all biological activity data for each compound across all the targets. The ligand page includes an option to display the results for InChIKey searching in Google, the utility of which has recently been described  (24). While the entire Key is used for exact-match searches of ChemSpider, the Google search uses just the inner 'layer' of 14 characters approximating to the basic molecular connectivity. It will thus retrieve all related entries with isomeric differences encoded in the outer layer of the Key. The results, typically returned in <0. 5 s with very high specificity, are the matches from over 50 million InChIKeys cached by Google from a wide range of databases and web resources.
IMPLEMENTATION The data are held in a PostgreSQL relational database (http://www. postgresql. org), with the exception of ligand structures and physico-chemical properties, which are stored in an Oracle database (Oracle Corporation, Redwood Shores, CA, USA). Curators use custom-built Java (Oracle Corporation, Redwood Shores, CA, USA) software to enter and edit data. The public web interface is implemented using HTML, CSS and JavaScript components generated dynamically on the server side by Java servlets and Java Server Pages. The web application runs in the Apache Tomcat servlet container (http://tomcat. apache. org/) on a Linux platform. Ligand structurebased searching is implemented with the Pinpoint chemical cartridge (Dotmatics Limited, Bishops Stortford, UK) and chemical structure editing capability is provided by the MarvinSketch chemical editor (ChemAxon Limited, Budapest, Hungary).
Ligand chemical structure formats and identifiers were generated using the Open Babel software  (25). IUPAC names were generated using JChem for Excel (ChemAxon Limited, Budapest, Hungary) and physico-chemical properties were generated using the Chemistry Development Kit  (26). Ligand images were created using the NCI/CADD Chemical Identifier Resolver from the National Cancer Institute (http://cactus. nci. nih. gov/chemical/structure). Small molecule ligands with similar structures were clustered using Pipeline Pilot (Accelrys, San Diego, CA, USA) and peptides with similar sequences were clustered using h-cd-hit, part of the CD-HIT Suite  (27).