dataesr/scientific-paragraphs-categorization huggingface dataset:
We applied our model to Indian Ocean yellowfin tuna, drawing on a dataset of repeat otolith readings that were collected during large mark-recapture experiments. To illustrate the interest of our method and assess the potential consequences for input data used in fish stock assessment models, we completed a series of simulations. Through Bayesian analysis, the uncertainty associated with age estimates derived from hard structures can be combined with supplementary information, e. g. , observations of the maximum size, or growth estimates for the same species in other areas, and expert knowledge to estimate growth.
Various age validation techniques exist including the release of tagged fish of a known age, nuclear or radiochemical dating, modal progression of catch-at-size data over time, and captive rearing  [25]. For yellowfin, the assumption of daily deposition was tested through a mark-recapture experiment of wild fish with oxytetracycline (OTC) in the length interval from 49. 7-131 cm F L. The result of this experiment suggested that the deposition rate estimated by the reader within this length interval is significantly different than the assumed single increment per day, with an increment-over-day ratio of 1:0. 939. The marginal posterior distribution revealed little variation in this ratio, suggesting that the reading method tended to systematically underestimate the actual fish ages. However, additionnal readings performed by new readers for some of the otolith data used in the present study however indicate a 1:0.
Acknowledgments The tuna tagging data analysed in this publication were collected under the  Indian Ocean Tuna Tagging Programme comprising the Regional Tuna Tagging Project of the Indian Ocean (RTTP)  funded under the 9th European Development Fund (9. ACP. RSA. 005/006 ) of the  European Union , and several small-scale tagging projects funded by the  European Union  and the  Government of Japan. We wish to acknowledge the contributions of all the people that have been involved in the  Indian Ocean Tuna Tagging Programme. We are grateful to  M. Herrera ,  C. Assan , and  A. Delgado de Molina  for data on yellowfin size measurements, and to  P. Dewals  and his team for their dedicated work. The manuscript benefited from helpful discussions with  F. Sardenne ,  M. J. Amande  `,  D. Gaertner ,  A.
Cell Culture MDCK cells (ATCC, Teddington, UK), strain II, were grown in DMEM with 10% FCS, streptomycin (100 mg/L) and penicillin (60 mg/L) at 37 °C and 5% CO 2. RPE-J cells  [51]  were maintained in DMEM with 4% FCS, 1% non-essential amino acids, 1% HEPES, streptomycin (100 mg/L) and penicillin (60 mg/L) at 32 °C and 5% CO 2. For polarity studies, 30,000 and 200,000 MDCK cells were plated and grown on coverslips in 24-well plates and on 24-mm (6-well plates) Costar Transwell filters (Corning, Sofia, Bulgaria) for five days, respectively  [52, 53]. The state of polarity was confirmed by measuring the transepithelial resistance (data not shown). For cell extraction studies, 30,000 MDCK and RPE-J cells were plated and grown in 24-well plates for 3 days. Transfections of MDCK cells were performed 24 h after cell seeding using the Effectene ® Transfection Reagent (Qiagen, Sofia, Bulgaria).
Bands were detected using Amersham Hyperfilm™ MP (GE Healthcare), films were scanned and sample signals from the same experiment present on the same blot were quantified with NIH ImageJ 1. 42 software (version 1. 42, NIH, Bethesda, MD, USA). We calculated Best1/actin ratios for each experiment, calculated mean ratios and SEM. , and tested for significance with Student's t-test.
45-µm step size scans. For all confocal data used for quantification, we acquired images using Leica TCS SP5 confocal microscope and LAS AF software (version 2. 6. 1, Leica Microsystems, Wetzlar, Germany), with a HCX PL APO lambda blue 63. 0 × 1. 40 OIL objective and 4-time zoom enlargement. Z-and Y-series image data were acquired in 0. 35-and 0. 30-µm step size sequential scanning respectively. Series data were analyzed using METAMORPH 7. 5. 1. 0 software (version 7. 5. 1. 0, Molecular Devices, Sunnyvale, CA, USA). Signals for Best1, β-catenin (basolateral marker)  [38]  and nuclei were quantified. Image stacks were used to generate intensity curves for all three signals along the different Z-focal planes. Ten randomly chosen cells from normal Best1 and each mutant were analyzed. The number of focal planes varies in the different cells depending on the slightly different cell height.
Acknowledgments The authors are grateful to  Kinga Bujakowska  ( Institut de la Vision ) for research discussion and advice,  María Lourdes Valdés Sánchez ,  Margarita Romero Durán  and  Simone Masalini  ( CABIMER ) for technical support,  Stéphane Fouquet  ( Institut de la Vision ) for help with confocal microscopy and  Christina Chakarova  ( UCL ) for assistance with manuscript preparation. This work was supported by  Agence Nationale de la Recherche  (Chaire d'Excellence to  Shomi S. Bhattacharya ),  Fondation Voir et Entendre  ( Young Investigator Grants  to  Christina Zeitz  and  Emeline F. Nandrot) ,  Centre National de la Recherche Scientifique (CNRS)  and  Fondation Bettencourt Schueller  (to Emeline F. Nandrot),  Université Pierre et Marie Curie-Paris6 (Bonus Qualité Recherche  to  Christina Zeitz ),  Foundation Fighting Blindness  (grant number  CD-CL-0808-0466-CHNO  to  Isabelle Audo ), Centre d'Investigation Clinique 503 recognized as a  Foundation Fighting Blindness Center  (grant number  C-CMM-0907-0428-INSERM04 ),  Fundacion Progreso y Salud  (to  Shomi S.
Acknowledgements This work was funded by the  French Ministry of Ecology and Sustainable Development. The authors would like to thank the technical teams of the laboratories LEHNA, LAQUE and POLDEN for their contribution to this work. They are also grateful to  IRSTEA-Lyon  which carried out the sampling of CSOs in the framework of the activities of  OTHU (Observatoire de Terrain en Hydrologie Urbaine) (Rhône-Alpes Region, France)  and the  Grand Lyon  for its financial support of  OTHU  activities.
MATERIALS AND METHODS The fish data used (age-specific abundance, fishing mortality, and maturity ogives) were extracted from assessments made using virtual population analyses (VPA; i. e. based on commercial catch data, Lassen & Medley 2001 and references therein for methods) tuned to the survey data. From these data we calculated the average fishing mortality (F) for each year. Table  1  summarizes the data used. Fig. 1  displays the general geographical localisation of each stock.
Walleye pollock Theragra chalcogramma of the Bering Sea and Gulf of Alaska systems Walleye pollock (Fig. S2  in the Supplement) currently constitutes the second largest single-species fishery in the world. Information on Eastern Bering Sea (EBS) pollock and Gulf of Alaska (GOA) pollock year-class strength is available back to 1964  (Ianelli et al. 2009 , Dorn et al. 2010). However, since catch data are uncertain prior to 1982, we used data only from 1982 onwards for EBS pollock and 1983 onwards for GOA pollock. For EBS pollock, the proportion of matures per age was given as a mean per age class. For GOA pollock, annual estimates of the agespecific proportion of mature individuals was available from 1983 onward. Fishing mortality is given by F 6-10 and F 5-8 for EBS and GOA pollock, respectively.
Environmental data Climate indices and regional-scale hydroclimatic variables were used as potential environmental drivers. Climate indices capture complex spatio-temporal variability into a simple metric and integrate largerscale climate processes and their variability  (Hallett et al. 2004). They have been shown to be good predictors for biological processes because they reduce complex space and time climatic variability into simple measures  (Stenseth et al. 2003). We selected a relevant large-scale climate index for each studied stock. For the NA hake stock, we used annual mean values of the Gulf Stream index (GULF; http:// web. pml. ac. uk/ gulfstream/data. htm), which is the first principal component of the position of the North Wall of the Gulf Stream  (Taylor 1995). For the SA hake stock we used an upwelling index (UI) obtained from 279 NEA cod NS cod EBS pollock GOA pollock NA hake SA hake BI hake VPA years a  1946-2010 1963-2011 1964-2010  1961-2010 1978-2008 1982-2008 1980-2008  Years used  1946-2000 1963-2007 1982-2004  1983-2002 1979-2003 1983-2003 1981- + + + + + + + N A O + + - - + + + GULF - - - - + + - P D O - - + + - - - UI - - - - - + - IDEA - - - - - - + Fishing mortality (F) + + + + + + + a VPA data were not reliable in the later years, so we used a shorter time series than available Table  1.
Data description of the stocks considered. NEA: Northeast Arctic; NS: North Sea; EBS: Eastern Bering Sea; GOA: Gulf of Alaska; NA: Northern Atlantic; SA: Southern Atlantic; BI: Balearic Islands; VPA: virtual population analysis; NAO: North Atlantic Oscillation; GULF: annual mean values of the Gulf Stream; PDO: Pacific Decadal Oscillation; UI: upwelling index; IDEA: hydro-climatic index based on the air-sea heat fluxes in the Gulf of Lions during winter. +: data used; -: no data the monthly mean of daily offshore Ekman transport values (-Q x ; m 3 s -1 km -1 ) offshore of the NW Spanish peninsula (see 'Methods' in  Lavín et al. 1991). For the BI hake we used the IDEA index, a mesoscale (regional) hydro-climatic index based on the air-sea heat fluxes in the Gulf of Lions during winter  (Massutí et al.
1996)  on a 1 × 1 degree grid and averaged over the area of each studied stock. Seasonal averages were calculated for each stock in winter (from December to February) and spring (from March to May) over each study area covering from early life stages survival to recruitment. For the Barents Sea we used yearly average sea tem perature measured monthly at 0-200 m depth (ST) on the Russian Kola meridian transect (33°30' E, 70°30' N to 72°30' N) (see references in  Hjermann et al. 2004). Time series of temperature for the NS cod and pollock stocks have been extracted over the distribution area of each stock from the extended reconstructed SST (NOAA_ ERSST_V3) data set provided by the Physical Sciences Division of the NOAA Earth System Research Laboratory (www. esrl. noaa. gov/ psd/). Data are available from 1960 to 2007 with 1 × 1 degree grid resolution for the cod from 1880 to 2009 and with 2 × 2 degree grid resolution for the pollock (based on the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) Sea Surface Temperature data).
Acknowledgements. We thank  The Research Council of Norway (RCN)  for funding the workshop Tropharct through the  MICO  project (grant no. 186310 ,  Match/Mismatch and Ecosystem ). This work is associated with the  IMBER regional program ,  Ecosystem Studies of Sub-Arctic Seas (ESSAS). We are greatly in debt to all the people who collected the data used in the study and made them available to us. J. M. D. , A. M. E. and D. Ø. H. acknowledge the support from the  RCN  through the  ADMAR  project (grant no. 200497/130 ). M. H. received support from  Marie Curie Intra-European fellowship  ( IEF ,  FP7-PEOPLE-IEF-2008 ; project No  236549 ). L. C. acknowledges the support from the  National Science Foundation -Research Coordination Network  grant no. 1140207. We thank  Leif-Christian Stige  for comments and advice.
Many plant families evolved mycoheterotrophy as a way of gaining nutrients, but the evolution of such nutritional shift has been little studied. We investigated mycorrhizal associations and a nutritional mode of two orchid species, a common ecological generalist Neottia ovata and N. cordata, a species of peatbog spruce forests. The species are closely related to a nongreen N. nidus-avis, associated with ectomycorrhizal Sebacinales, clade A. We investigated the course of germination at 18 sites in the Czech Republic, analyzed fungal spectra of both seedlings and 39 N. ovata and 21 N. cordata adults growing in 32 European sites in different habitats and measured the natural content of stable isotopes. Seeds of both species suffered high mortality during two years of in situ germination. Whereas N.
Neurons were then fixed with paraformaldehyde and the second anti-rabbit Alexa Fluor-588 antibody was added. Green images correspond to expression of Homer1C-GFP. Immunofluorescence was visualized with an up-right epifluorescence microscope (Leica Microsystems, Wetzlar, Germany). Image acquisition was performed with a LEICA camera and the Metamorph analysis software. Co-localization images were analysed with the MATLAB software as in  Jaskolski et al (2005). To compute the degree of apposition between expressed myc-GluK5wt and postsynaptic clusters (Homer1c-GFP), a plug-in was developed on Metamorph software. Fifteen pixels regions corresponding to synapses (green from Homer1C-GFP and spiny shape) were selected on the images, allowing identification of synaptic structures as individual objects.
Diffusion coefficients were calculated by a linear fit of the first 4-8 points of the MSD plots versus time depending on the length of the trajectory within a certain compartment. The resolution limit for diffusion was 0. 0075 mm 2 /s whereas the localization precision was B40 nm. Statistical values are given as medians ±25%/75% interval. Statistical significances were performed by using GraphPad Prism software. Non-Gaussian distributed data sets were tested by Mann-Whitney t-test. Indications of significance correspond to P-values o0. 05 (*), Po0. 005 (**) and Po0. 0005 (***).
Acknowledgements This study was supported by the  Centre National de la Recherche Scientifique , the  Conseil Regional d'Aquitaine , the  European Commission  (EUSynapse Project, contract no. LSHM-CT-2005-019055 ;  EIF Fellowship  awarded to MC; IIF Fellowship awarded to PO and  ERC  Grant  232942  Nano-Dyn-Syn to DC) and the  Agence Nationale de la Recherche  (contract MossyGlu). We thank  Jessica Ragues  for producing the plasmid DNAs,  Elisabeth Normand ,  Alice Vimeney ,  Noelle Grosjean  and  Audrey Lacquement  for taking care of the mice and for neuronal cultures. Confocal microscopy was performed at the  Bordeaux Imaging Center. The help of  Philippe Legros ,  Christel Poujol  and  Laure Malicieux  is acknowledged. We thank  Nelson Rebola  and all members of the  Mulle lab  for suggestions and discussions.
INTRODUCTION Assessing the sustainability of human activities is becoming a worldwide major concern. In this paper, we consider examples that belong to the agriculture domain such as agronomy, ecology, economy, sustainability modeling, etc. Researchers in the agriculture domain do not only seek to assess and improve the performances of farms, but also their sustainability, through many indicators. The development and computation of such indicators require being expert in several areas. Indeed, some aspects to be treated may be e. g. soil biology, animal welfare, waste management, economic durability, sales, etc. For this, researchers from all over the world compute data from many sources, both local and from the Internet, that cannot be used directly, e. g. data from RDBMS (Relational DataBase Management Systems) and complex models.
Consequently, many computer programs and database queries have been developed for interperting data. However, they are specific to each data source but experts need to reuse the code already written e. g. to extract data from other sources. That's why they need a common platform not only for experts design support but also for computers to assist and/or automate the development and reuse of treatments, models, etc. Large amounts of data and treatments that are stored on the Internet and from disparate sources must be computed, according to a common formalism that we propose in this paper. The remainder of this article is structured as follows: in section 2 we provide a state of the art. In section 3, we present the features of our approach. Then, in sections 4 and 5, we respectively provide definitions and algebra we have defined.
Combining the approaches Our goal is not only to analyze the requirements of a system. Our platform has also to find the ad hoc compositions of treatments solving both users' and computers' requirements, to formalize models, algorithms and data to assist or automate their reuse, to assess them, and consequently to be both worldwide researchers and machine understandable. In  (Dantan, J. , Pollet, Y. , Taibi, S. , 2012) , two kinds of virtual properties have been defined, as an extension of OWL (W3C, 2012): these virtual properties, attached to core OWL ontology classes, may be hierarchically organized goals that have to be evaluated. We provide a goal-oriented model that is linked to the Semantic Web properties. We combine a core OWL ontology extended by virtual properties with a goal ontology that contains hierarchically organised goals and sub-goals.
OVERVIEW We propose a goal-oriented model where each goal is attached to both the domain ontology, as a "goal property" and to a semantic goal-oriented service, according to the following procedures: (1) A core OWL ontology which is part of the Semantic Web is used to model the considered domain. We attach goal assessments as classes' properties of the selected core ontology, which consists of an extension of core ontology. (2) Formalization of data models, algorithms and non-functional properties in a common formalism for expressing goals and their assessment, by defining an algebra and a high level language that is both machine and human understandable. (3) Linking with the Semantic Web. Every data models and algorithms are semantically indexed and each goal property is defined thanks to an a URI (Uniform Resource Identifier).
This explains why very few of the large number of promising biomarkers and drug targets make their way to effective clinical practice, and thus why costs in drug development and healthcare have escalated  [28]. During the past year, great advances have been made in overcoming these hurdles, as witnessed by two reports on the longitudinal followup of individuals and inte gration of extensive datasets  [29, 30]. In the first case, an integrated, dynamic 'omics profile for one person was recorded regularly over more than a year, combining genomic, transcriptomic, metabolomic and immuno logical profiles. It revealed a risk for type 2 diabetes and pathways and molecular mechanisms distinctive to the healthy and disease states for that individual  [29]. The second case is a report on a decadelong quantitative recording of blood and stool biomarkers, tracking of nutrition, exercise, sleep and stress, combined with personal genetics and microbiome assessment of one individual.
It detected a persistent source of inflammation that turned out to be an early presymptomatic sign of the development of lateonset inflammatory bowel disease  [30]. These are important steps for these individuals, who were alerted on their potential or actual health risks earlier than they would have been in a classical medical situation. Most importantly, the two studies indicate that collection of datasets in regular time series and their combination with background genetic or genomic data on single individuals is sufficiently powerful to overcome the hurdles discussed above. Such findings should, in turn, inform studies in pharmacogenomics so as to tailor personalized treatments for larger groups of patients  [23]. They should also facilitate the development of panels of indicators for disease control as part of decision support systems for the monitoring of prominent complex diseases, such as chronic obstructive pulmonary disease, by patients and their physicians  [31].
Selection of X-ray structure and model building The primary sequences of human TWIST1 (Q15672), TWIST2 (Q8WVJ9), E12 (P15923-1), HAND1 (O906004) HAND2 (P61296), NEUROD1 (Q13562), E47 (P15923-2), and MYOD1 (P15172) were downloaded from the UniProtKB/Swiss-Prot website. The mouse NEUROD1 and E47 sequences were obtained from the 2ql2 PDB files. Primary sequences, restricted to the bHLH domains, were aligned with the ClustalW 2. 0 software  (Thompson, Higgins, & Gibson, 1994). The X-ray structures of the human MYC/MAX dimer (PDB code lnkp)  (Nair & Burley, 2003) , mouse MYOD1/ MYOD1 complex (PDB code lmdy)  (Ma, Rould, Weintraub, & Pabo, 1994) , and mouse NEUROD1/E47 complex (PDB code 2ql2)  (Longo, Guanga, & Rose, 2008)  were downloaded from the RCSB protein data bank. Two X-ray structures of mouse NEUROD1/E47 have been reported (2ql2A/2ql2B; 2ql2C/2ql2D), which slightly diverge (root-mean-square deviation, RMSD).
Minimizations (>10,000 steps with the conjugated gradient algorithm) were carried out with the Sybyl-X 1. 1 software package, elaborated by the Tripos company. We applied the Tripos force field with the Gasteiger-Marsilli partial charges and a dielectric constant of 80 to simulate an implicit water phase (the dielectric constant of water is 20. 10 at 20 °C). No restrains was applied to our models. This step principally refines and corrects the positions of residue side chains. The effects of minimization on the homology models were estimated approximately by calculating their RMSDs before and after the minimization step. In the heterodimer, the RMSDs of the E12 and TWIST1 peptides were 1. 58 and 1. 97 Å, respectively. In the homodimer, the RMSDs of the two TWIST1 peptides were 3. 21 and 1.
Molecular dynamics analysis The established homology models (including DNA sequences) were visualized with the VMD1. 9. 1 software  (Humphrey, Dalke & Schulten, 1996; Jorgensen, Chandrasekhar, Madura, Impey & Klein, 1983 ). The resulting model was inserted into a parapipedic TIP3P solvent box by means of the add solvation box module of the VMD 1. 9. 1 software. A distance of 15 Å was set between the surfaces of the protein and the limit of the solvent box. Conditions were computed to reach neutral charges before adding sodium and chloride to concentrations corresponding to physiological conditions. The model was minimized with the NaMD 2. 8 bl software for 1000 steps before the molecular dynamics simulations  (Phillips et al. , 2005). It was computed on a 144 xeon core CPU cluster supercomputer (SGI Altix).
The loops of the TWIST1/TWIST1 complex reach equilibrium at 5. 5 ns (Figure  S1(B) ). This stabilization is likely due largely to interaction with DNA, as described below. We, then, investigated whether the conformation of the lateral loops might depend on the strand of DNA associated with the monomer, as E-boxes are asymmetrical. For this, we inverted the orientation of the DNA in the TWIST1/TWIST1 model, and carried out an additional 10-ns dynamics simulation. This inversion was found to affect the loop conformations only slightly, as symmetrical interactions between Arg118 and the DNA were found (Figure  S2 ). We conclude that the divergence between T-and E-loops within the TWIST homodimeric complex is unlikely due to sequence difference between the two DNA strands. Taken together, our data demonstrate that TWIST1 complexes differ significantly in the conformation of their interhelical loops.
Taken together, these data demonstrate that H-bonds are determinant in maintaining interhelical loop conformation and contribute to determining the affinity of TWIST1 complexes for E-boxes. In support of this view, the S144R mutation in TWIST1 abolishes the stable binding to DNA of both the TWIST1 homodimer and the TWIST1/ E12 heterodimer (Figure  S3 )  (El Ghouzzi et al. , 2001; Maia et al. , 2012) ). Interestingly, in both the T-and E-loops of the TWIST1/TWIST1 and TWIST1/E12 dimers, the highly conserved residue Arg118 (Figure  1 ) additionally establishes stable H-bonds with both the O2P group of Cyt (+4) and the O2P group of Ade(+4 ⁄ ) bases (Figure  7 ). The fourth base of the E-box is, thus, important in stabilizing TWIST1 complex binding to DNA. When the DNA is placed in the opposite orientation in the TWIST1 homodimeric model, the H-bonds between residue Arg118 and the fourth base of the E-box (Ade(+4 ⁄ ) or Thy(+4)) are conserved (Figure  S2(B) ).