dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2001; Muller et al. , 2006; Haegler et al. , 2009). Conceived for computer graphics applications, Cityengine is a system able to model a city starting from 2D information (GIS, plans and so on). Therefore procedural modelling of a city is based on union of different procedures  (Ferdani et al. , 2001) : 1) image based, basing on terrain maps and aerial images of the buildings allocation; 2) L System, computer language representing the complexity of an organism or a building by few parameters and an growth algorithm; 3) Parametrical rules, giving the possibility to choose the detail and realism level. Moreover, different steps are necessary to create a 3D city. 2D information is transformed in 3D streets, blocks and parcels. In these parcels are allocated the buildings created by 3D extrusion operations and the facades are textured, using a procedural approach based on L-system.
It is possible to establish the shape grammar rules by scripts for the definition and the control of geometrical parameters and hierarchical relationships between the architectonic elements of buildings and their automatic generation. During the scripting step, a correlation matrix is created for giving a credible aspect to the model. This matrix assigns dimensions and different textures to the buildings in random way established by the user. An interesting test case modelling by the CityEngine software is Procedural Pompeii. This project has been carried out within the European Project Cyberwalk. The ancient city of Pompeii has been reconstructed mixing real surviving and excavation data with GIS information such as population density, land usage, street network and building footprints. The building geometries are created by shape grammar rules derived from photos and plans of remaining buildings, archaeological excavation data, and historical paintings.
The Pompeii model has been completely generated with CityEngine. The same methodology is adopted by VHLab CNR-ITABC to rebuild the ancient landscape of Bologna in Roman and Etruscan times  (Ferdani et al. , 2011). The approach of this project starts from the ruins 3d acquisition by RBM and IBM techniques and then it resorts to procedural modelling to rebuild the potential aspect of ancient city in different ages. These two test cases demonstrate that procedural modelling is especially used in archaeology to reconstruct ancient urban environments for communication, learning and educational purposes. We have tested the ability of Cityengine software to create different opus typologies. The outcome is not really satisfying because we want to create the masonry in parametric and interactive way, and then adapting them to the photogrammetric data.
Harmful algal blooms (HABs) occur worldwide, causing health problems and economic damages to fisheries and tourism. Monitoring agencies are therefore essential, yet monitoring is based only on time-consuming light microscopy, a level at which a correct identification can be limited by insufficient morphological characters. The project MIDTAL (Microarray Detection of Toxic Algae)-an FP7-funded EU project-used rRNA genes (SSU and LSU) as a target on microarrays to identify toxic species. Furthermore, toxins were detected with a newly developed multiplex optical Surface Plasmon Resonance biosensor (Multi SPR) and compared with an enzyme-linked immunosorbent assay (ELISA). In this study, we demonstrate the latest generation of MIDTAL microarrays (version 3) and show the correlation between cell counts, detected toxin and microarray signals from field samples taken in Arcachon Bay in France in 2011.
Microarrays offer the most expeditious method to have high sample throughput with highly accurate species detection, in a universal approach  [6] [7] [8]. In the FP7 EU project, MIDTAL (Microarrays for the Detection of Toxic Algae), an earlier protocol for detection of toxigenic microalgae by Gescher et al. [9]  was optimized. Microarrays (as phylochips) detect multiple species simultaneously using species-specific probes that have been applied primarily for the detection of bacteria  [10] [11] [12] [13]. At present, 140 probes for various toxic algal species at various taxonomic levels are spotted onto the current generation of the MIDTAL microarray. As part of the MIDTAL project, the primary goal was to be able to infer cell numbers from the molecular signal to provide an early warning system for toxic algae.
Because the MIDTAL microarray is a universal array that can be used globally, it offers a real possibility of detecting invasive species, especially in view of global warming where warm temperate species are moving northward, e. g. , Gamberiodiscus. In this study, we show the effectiveness of using microarrays for the detection of toxic algae and its combination with toxin detection. We compare these results with light microscopy data from a regular French monitoring network of toxic phytoplankton. The microarray used in this study represents the third generation array developed within the EU-MIDTAL project. In generation one, probes (18-22 nt) developed for Fluorescent in situ Hybridization (FISH) were used directly; in generation two, these FISH probes, and any newly designed probes, were lengthened to 25 or more nt; in generation three, an additional poly-T spacer to lift the probes farther above the surface was tested and optimized (Figure  1 ).
Microarray Design Probe design was done with the open software package ARB  [18]. All oligonucleotides including the positive and negative controls were synthesized by Thermo Fisher Scientific (Ulm, Germany) with a C6 aminolink at the 5' end of the molecule. The probes had a length between 18 and 25 nt and a 15 nT-long poly (dT) tail following the NH 2 link at the 5' end. Table  2  shows a list of the probes and their targets. The complete hierarchy for each probe can be found in the GPR-Analyzer which is available online at http://folk. uio. no/edvardse/gpranalyzer. The probe sequences are patent pending and a commercial kit will soon be available from Kreatech containing the array and all reagents for hybridization. Epoxy-coated slides (Genetix or Schott) of MIDTAL version 3. 2 were printed using a pin printer VersArray ChipWriter Pro (Bio-Rad Laboratories GmbH, Munich, Germany) and split pins (Point Technologies, Inc.
Data Analysis Obtained fluorescent signals and the surrounding background intensity were calculated by superimposing a grid of circles (midtal_ver32_20110429. gal or MIDTAL_V3. 3. gal) onto the scanned image using the GenePix 6. 0 software. First results were processed through the phylochip analyzer program to generate a hierarchy file to establish the hierarchical levels of the probes on the chip  [19]. The hierarchy file and hybridization results were then progressed with the GPR-Analyzer version 1. 27 and the hierarchy file version 1. 06  [20]. A signal-to-noise ratio (S/N ratio) above two was taken as a cutoff for a positive signal. To compare values from different hybridizations, signals were normalized using the internal control DunGS02_25_dT (corresponds to Dunaliella tertiolecta), and replicates averaged.
Furthermore, 7,000 cells• L -1 of Pseudo-nitzschia (mainly from the -thin‖ group) were observed in sample 2A. Except for the aforementioned Pseudo-nitzschia multistriata (400 cells• L -1 ), no potentially toxic species were observed at the end of August (sample 3A). In both October samples (4A and 6A), it was possible to identify Heterosigma akashiwo with 600 and 400 cells• L -1 , respectively. In the late October sample (6A), 30 cells• L -1 of Dinophysis caudata were also counted. Despite few events of potentially toxic algae blooms during our study period, we can point out the presence of five genera in our samples (Pseudo-nitzschia, Heterosigma, Prorocentrum, Alexandrium, and Dinophysis) that are all represented by probes on the MIDTAL microarray.
Conclusions The third generation of the MIDTAL microarray with its improved protocols has great potential to be used as a monitoring tool for toxic algae, even in non-bloom situations, although improvements and tests are still needed. The probes on the MIDTAL microarray have been designed from a global database and the specificity tests done on the probes were made from global isolates. Thus, the MIDTAL microarray can be regarded as a universal microarray that can be used globally. Its specificity has been tested at eight sites across Europe within the MIDTAL project over a two-year period and in no case did it fail to detect the presence of a toxic species when cross-validated with the toxin array. Our results show the advantage of combining the MIDTAL microarray with toxin detection, especially for detecting species either not counted in the cell counts because of low volume or poor preservation, or because they are new to the area, i.
Acknowledgments YDA thanks her crew members ( Laurent Letort  and  Francis Prince ) for sampling in Arcachon Bay. Cell counts were supplied by  Nadine Masson Neaud  and  Daniè le Maurer  from  IFREMER/LER Arcachon (Ifremer/Quadrige²/Rephy DATA). This work was funded by the  EU 's  7th Framework Program  ( FP7-ENV-2007-MIDTAL-201724 ).
The FunFOLD2 server is a new independent server that integrates our novel protein-ligand binding site and quality assessment protocols for the prediction of protein function (FN) from sequence via structure. Our guiding principles were, first, to provide a simple unified resource to make our function prediction software easily accessible to all via a simple web interface and, second, to produce integrated output for predictions that can be easily interpreted. The server provides a clean web interface so that results can be viewed on a single page and interpreted by non-experts at a glance. The output for the prediction is an image of the top predicted tertiary structure annotated to indicate putative ligand-binding site residues. The results page also includes a list of the most likely binding site residues and the types of predicted ligands and their frequencies in similar structures.
FunFOLD The FunFOLD method (  2 ) is based on one key concept: proteins containing ligands within the PDB, with a similar fold as the 3D model of the target protein being studied, are likely to having similar binding sites (2). A ligand is defined as a biologically relevant molecule, which binds to a structurally elucidated enzyme in the PDB. The FunFOLD algorithm uses the TM-align  (19)  method to superpose templates containing biologically relevant ligands with the predicted 3D structure from IntFOLD2-TS. Each model-to-template superposition is subsequently used if the TM-score 0. 4 [TM-scores from 0. 4 to 0. 6 have previously been shown to mark the transition from unrelated to significantly related folds  (20) ]. The superpositions are then combined and reoriented using a PyMOL script (http://www.
5 A ˚. The output from the FunFOLD algorithm is a list of putative ligand-binding site residues plus a list of the ligands within the binding site cluster  (2). The FunFOLD algorithm has been extensively benchmarked on both the CASP8 and the CASP9 data sets (2). An early implementation of the algorithm was used in the CASP9 competition (2010), where it ranked amongst the top 10 methods  (21). For a more in-depth description of the FunFOLD algorithm and methods benchmarking, see Roche et al. (2). Furthermore, the FunFOLD algorithm has recently been used in two large-scale genomic studies  (7, 22).
CASE STUDY-AMINOPEPTIDASE N FAMILY PROTEIN (PDB ID 4fgm AND CASP10 T0726) The aminopeptidase N family protein Q5QTY1 from Idiomarina loihiensis (PDBID 4fgm and CASP10 target T0726) provides an example of output from the FunFOLD2 server (Figure  2 ), where using FunFOLDQA to rank predictions results in improved prediction quality. Figure  2  highlights the predicted binding site residues in green (B and C), over-predictions are shown in red and observed residues in blue (A). The ligands predicted (B and C) and observed (A) are shown in white. The top FunFOLD2 prediction correctly predicts the binding site residues (273, 277 and 307), with a predicted MCC score of 0. 882 and predicted BDT score of 0. 801 and per residue scores (r = histidine (HIS); n = 277; j I = 0. 212; O = 000; N = 000; P = 000, r = glutamic acid (GLU); n = 307; j I = 0.
LIMITATIONS Predicting ligand-binding site data is a difficult task, and there are several limitations to current prediction methods. The following is a list of the most common limitations specific to the current implementation of the FunFOLD2 server: (i) If the server is unable to build a starting model for the target sequence, then it cannot predict any ligand-binding sites, although, fortunately, for the majority of protein targets, a reasonable 3D model can be obtained. (ii) If no structural similarity can be found, between the target model and structurally elucidated proteins with bound biologically relevant ligands, then a prediction is not made. (iii) Only one ligand-binding site is predicted per target sequence-the site with the largest identified ligand cluster. However, the server does also provide the data showing all putative ligand clusters, and these clusters can be made visible to users on the results page using the Jmol plugin.
CONCLUSIONS The FunFOLD2 server provides biologists with an intuitive interface for the prediction of protein-ligand interactions from amino acid sequences. Graphical output and plug-ins are provided to facilitate interactive visualization of predicted interactions in 3D, and machine-readable files are provided for developers. The algorithms within the FunFOLD2 server have been independently tested in the recent international CASP10 competition where the method was found to rank amongst the top few. Additionally, both the FunFOLD (2) and FunFOLDQA algorithms have been extensively benchmarked on both the CASP8 and CASP9 data sets  (1, 2).
FUNDING Studentship  from the  University of Reading ,  MRC Harwell  and the  Diamond Light Source. (to M. T. B. ). This research leading to these results has received funding from the  European Union Seventh Framework Programme  [FP7/2007-2013] under grant agreement No. [246556 to D. B. R. ]. Funding for open access charge: Genoscope,  Institut de Ge´nomique ,  Commissariat al 'Energie Atomique et aux Energies Alternatives. European Union Seventh Framework Programme  [FP7/ 2007-2013] under grant agreement No. [246556].
Moreover, the identification of fewer perfect matches in EST databases for the shuffled genes (24%) compared with the conserved genes (48%) is in favor of transposed genes being pseudogenes known to be less expressed. We conclude that transposed and deleted duplicated genes account, to a great extent, for the reported subgenome dominance in paleopolyploid grasses  (Abrouk et al. 2012). This raises the hypothesis that underexpressed genes in sensitive subgenomes are more likely to become deleted or transposed, leading to long-term pseudogenization, a process defining subgenome dominance, because these genes may be less important for maintenance of a perfect gene product balance and are thus less essential for fitness  (Freeling et al. 2012). Overall, gene deletion or movement may appear as a particularly active phenomenon after polyploidy and may then act preferentially on the sensitive genome compartments, making them more labile than the orthologous dominant counterpart.
Acknowledgments This work was supported by grants from the  Agence Nationale de la Recherche (Programs ANRjc-PaleoCereal , ref:  ANR-09-JCJC-0058-01  and program  ANR Blanc-PAGE , ref:  ANR-2011-BSV6-00801 ). The authors gratefully acknowledge  Eric Tannier  ( INRIA ,  UMR CNRS 5558-LBBE ,  UCB Lyon 1, Villeurbanne, France ) and  Cedric Chauve  ( Department of Mathematics, Simon Fraser University, Burnaby, BC, Canada ) for providing support in using the ANGES software for AGK and ancestor intermediate reconstruction.
Partners in WP 4 will conduct a school-based randomized controlled trial in ten schools in Chochaengsao Province, Thailand. The trial protocol is publicly available  [28]. To complement this trial, we will also conduct laboratory-based studies on impregnated school uniforms at Research area 3 with another three work packages focuses on the risk of introduction of dengue into currently non-endemic areas. WP 6 will collect clinical and virological data in travelers returning to Europe from dengue-endemic countries. WP 7 has set out to explore the role of Aedes albopictus in southern France, but will also investigate control strategies of Aedes albopictus in Europe. WP 8 will work on predictive risk modeling and maps under different future climate scenarios in Europe. Cross-cutting work packages were added with skills and expertise in research conduct and data management (WP 9), geo-spatial modeling and risk maps (WP 10), and economic evaluation and evidence-informed policy making (WP 11), mainly contributing to WP 1 and WP 4, but also to other work packages if needed.
e. , subclinical vs. symptomatic) and the vector-virus genetic constitution. To address research questions around the evolving epidemiology of dengue, the N Development of study Case Report Form (CRF) and initiation of study at first site N Development of centralised computer database for local data entry at each site N Recruitment of 50% of sample size with data entry completed N Evaluation report on warning signs and on the practical application of the 2009 WHO dengue case classification scheme, compared with the DHF/DSS classification scheme N Molecular diagnosis of dengue patients underway N Serological assays optimised and characterised N Characterization of the immunogenicity of novel vaccine constructs N Identification of reliable, sensitive, practical epidemiological indicators N Recommendations for outbreak response tools for effective vector control tools N Recommendations for outbreak response plans for health care service reorganisation N Integrated Surveillance Model N Contemporary/baseline map of dengue occurrence N Future map of dengue occurrence (2020, 2050, and 2080) N Consensus documents on (1) research process and (2) policy recommendations N Policy briefs on the organisation of dengue surveillance N Documented quality assurance in all WPs II.
Acknowledgments We are grateful for the input of the partners involved in the three EU-funded consortia. IDAMS:  Thomas Jaenisch ,  Thomas Junghanss ,  Kerstin Rosenberger ,  Jaswinder Kaur  ( Section Clinical Tropical Medicine, Heidelberg Universty Hospital );  Simon Hay ,  Janey Messina ,  Adrian Hill  ( Oxford University );  Bridget Wills ,  Cameron Simmons ,  Marcel Wolbers ,  Jeremy Farrar  ( Oxford University Clinical Research Unit, Vietnam );  Phil McCall  ( Liverpool School of Tropical Medicine );  Antonio Lanzavecchia ,  Federica Sallusto  ( Institute for Research in Biomedicine, Bellinzona, Switzerland );  Axel Kroeger ,  Silvia Runge-Ranzinger  ( TDR-WHO );  Lucy Lum  ( University of Malaya Medical Center );  Ida Safitri  ( Gadja Madah University, Indonesia );  Varun Kumar  ( Angkor Hospital for Children, Cambodia );  Maria Guzman  ( Instituto Pedro Kouri, Cuba );  Gabriela Maron ,  Ernesto Pleitess  ( Hospital National de Ninos Benjamin Bloom, San Salvador );  Andrea Caprara ,  Bruno Benevides  ( State University of Ceara, Brazil );  Willy Wint (Environmental Research Group Oxford Ltd.
2 million in 2011. Presently over a million observations are being made each year  (Sabine et al. , 2010). In order to deal with these data effectively and to maximise their scientific use, the international ocean carbon research community initiated the Surface Ocean CO 2 Atlas (SOCAT) project in 2007  (IOCCP, 2007). The aims of SOCAT were threefold. Firstly, SOCAT aimed to merge all available surface ocean CO 2 data into one uniformly formatted, quality controlled, publicly available database with regular updates. The second aim of SO-CAT was to secure the long-term storage of each data set together with its required documentation (metadata). Finally, the community sought to realise a transparent and traceable approach for the handling, quality control and integration of surface ocean CO 2 data, which may be managed by the community on a routine basis in the future.
Over the past decades several attempts have been made to establish a global surface ocean CO 2 database. In the late 1990s, Taro Takahashi from Lamont-Doherty Earth Observatory (LDEO) compiled an initial data set and updated this collection in 2002 and every year from 2007 onwards  (Takahashi et al. , 1997 (Takahashi et al. , , 2002 (Takahashi et al. , , 2009a (Takahashi et al. , , 2011)). The primary reason for this effort was the creation of global climatologies of airsea CO 2 fluxes  (Takahashi et al. , 1997 (Takahashi et al. , , 2002 (Takahashi et al. , , 2009b)). This LDEO database was made public in 2007 and is currently being updated on an annual basis. The data treatment is based upon Takahashi's long experience. The LDEO database includes pCO 2 from discrete and continuous measurements.
The most recent version of the LDEO data set has 5. 2 million pCO 2 data from the global oceans and coastal seas from 1957 to 2010  (Takahashi et al. , 2011). In 2001, Bakker began to assemble a surface ocean CO 2 data set by putting public data from CDIAC into a uniform format, as part of the European Union (EU) project ORFOIS (Origin and fate of biogenic particle Fluxes in the Ocean and their Interaction with the atmospheric CO 2 concentration as well as the marine Sediment). Pfeil and Olsen streamlined and expanded this effort within the EU project CarboOcean from 2005 onwards. They compiled public surface ocean CO 2 data held at CDIAC, PANGAEA -Data Publisher for Earth & Environmental Science (an International Council for Science (ICSU) World Data Center, formerly the World Data Center for Marine Environmental Sciences, WDC-MARE) and elsewhere into a common format f CO 2 database based on the recommended formats for data and metadata reporting  (IOCCP, 2004   The Surface Ocean CO 2 Atlas was initiated at the Surface Ocean CO 2 Variability and Vulnerability (SOCOVV) meeting by the international ocean carbon research community (Table  1 )  (IOCCP, 2007).
A series of meetings was held in which SOCAT gradually took shape and in which the regional groups coordinated their work (Table  1 )  (IOCCP, 2007 (IOCCP, , 2008 (IOCCP, , 2009a (IOCCP, , b, 2010a, b), b). The SOCAT community evaluated existing data compilations and selected the data collection by Pfeil and Olsen as the basis for SOCAT  (IOCCP, 2008). The focus for SOCAT has been the assembly of publicly available data (including metadata), standardisation of the file formats, recalculation of consistent and uniform surface water f CO 2 data, and basic and secondary level quality control (Sects. 3, 4 and 5). SOCAT is independent from the LDEO database  (Takahashi et al. , 2011) , but has a large overlap in its original data. SOCAT only includes surface water CO 2 values, measured in near-continuous operation or in discrete samples with an equilibrator system or a spectrophotometer and reported as xCO 2 (CO 2 mixing ratio), pCO 2 or f CO 2 (Sect.
This definition was later replaced by a criterion of distance from a major land mass (less than 400 km) in order to better reflect the environmental significance of these regions as continental margins. Figure  3  shows these oceanic and coastal regions in SOCAT. SOCAT has been a large, complex undertaking and has involved activities focused on: data retrieval, assembling data in a uniform format, recalculating surface water f CO 2 using the same agreed-upon protocol, defining SOCAT QC criteria, developing the QC cookbook and Matlab QC code, making SOCAT available via the Live Access Server (LAS) for QC and public release, data QC, gridding SOCAT, making SOCAT documentation and products available via the web, designing the SOCAT logo, internal communication, organisation of SOCAT meetings, and liaising with the international marine carbon community.