dataesr/scientific-paragraphs-categorization huggingface dataset:
Previous important efforts have been made in the past to curate the automated functional annotation  [7]. These data are publicly available, but are not well integrated into major genomic databases such as NCBI and EBI. To streamline the new nomenclature initiative from the sNCGGa, a set of directives, addressing the most important issues, has to be provided to allow a better integration of the various, diverse resources into an improved global annotation of the grapevine genome, both at the structural and functional levels. These directives are aimed at facilitating exchanges between international genomic repositories to assist the analysis of gene experimental functional data and comparisons with other species. In addition to the sequencing of the nearly homozygous PN40024 genome, other genomic resources for V.
vinifera, and related species, continue to be generated, including the sequencing of the genomes of other varieties  [8] [9] [10] , EST sequencing, integrated genetic maps, and the whole genome re-sequencing for polymorphism discovery of other Vitis varieties and species  [11]. The EST and genome resources have permitted the design of a wide variety of microarrays for large-scale mRNA expression profiling studies (for example:  [12] ), but microarrays are being replaced by RNA-seq (for example:  [9] ). A majority of the expression data are maintained in the PLEXdb database  [13]. However, heterogeneity in the design of the microarray platforms, both in terms of the version of the annotation and in technical design, requires considerable bioinformatic effort to identify the probes or probesets corresponding to a unique gene.
The grapevine genomics community at large is mostly structured around the International Grape Genome Program (IGGP; www. vitaceae. org) whose mission is to facilitate the networking of grapevine researchers in order to develop common and publicly available resources. These resources facilitate the elucidation of the genetic and molecular basis of biological processes in Vitis and should lead to a more efficient exploitation of the Vitis biological resources for the development of new cultivars and clones that have improved quality and reduced economic and environmental costs. It may also allow for more efficient vineyard management. It is therefore the IGGP's objective to provide a common platform for continuous improvement of the annotation of grapevine genes. This objective will be coordinated by the Supernomenclature Committee for Grapevine Gene Annotation (sNCGGa), and was supported by the Grape Research Coordination Network (funded by the United States National Science Foundation in the USA).
The first milestone presented here is the development of a standardized protocol for gene naming, with names that have to be unique, consistent with other plant models and sustainable. This report proposes guidelines for the nomenclature of the genes from the latest version of the gene structural annotation, promoted by the COST (European Cooperation in Science and Technology) ACTION FA1106 (funded by the European Union), and performed on the assembly (V2) of the scaffold from the 12X version of the reference genome performed in a collaboration between the Institut National de la Recherche Agronomique (INRA) and the Istituto di Genomica Applicata (IGA). The automatic annotation of the genes was performed with the Eugene software  [24]  at the Vlaams Instituut voor Biotechnologie (VIB) and released through the ORCAE website that will be used for community annotation  [25].
In the case of splitting gene models, a new Locus-ID will be created and attributed to the new gene model. As such, Locus-IDs should not be seen as positional and derived products; however, transcripts and proteins will remain linked to these Locus-IDs. These rules can be virtually applied to any objects that are absent from the reference genome, such as genes that are only identified in other cultivars or Vitis species. Non-reference genes can then be  referenced with their chromosome number (or "00" if unknown) and a numeric code can be stored in the ORCAE platform  [25]  that will be used for community annotation. Taking into account previous experiences acquired through the previous grapevine locus ID schemes  [26]  and structures defined in other species, an ID containing the following elements was retained: Taxonomy ID/ Chromosome number/Object type/Numeric code/Sequence variant/Version.
Such an event will not impact the nomenclature; even if it involves chromosome changes, the old Locus ID will be stored as a synonym and a new Locus ID will be allocated, while in the case of a change of scaffold orientation, nothing would change. With a length of 5 digits for all the objects per chromosome (up to 99,999), the risk of running out of numbers is very low. The ORCAE platform  [25]  being used by the grapevine community can automatically handle any changes to ID numbers, decreasing the risk of errors. Sequence variant This segment, which shall be preceded by a hyphen, will be used to discriminate molecular variants (allele, splice variant) that map to the same locus. The code can be numeric or alphabetic (e. g. for cultivarspecific polymorphism). If no allelic variant is present, one should refer to the primary sequence from the reference genome.
In Arabidopsis and rice the use of species-specific prefixes (At, Os) for the symbol and the full name in the official name is discouraged because of redundancy with species information already known elsewhere (in the Locus ID, for example), the same shall apply for Vitis. However, it could be added when specifically referring to the Vitis gene in publications, with the vinifera prefix being Vvi and the other prefixes as shown in the VIV catalogue  [29]. Although Vitis vinifera genes were named with the vv (or Vv) prefix, this creates confusion with the bacteria Vibrio vulnificus, whose genome was published before the grapevine and "locked" the vv prefix into major databases. A two-letter code is also too short for discrimination between Vitis species. The intention of this paper is to strive to a consistent naming scheme that would avoid redundancy and confusion within and across gene families.
Other sequences were identified and classified up to HT24  [3]. However, in Arabidopsis this family is named sugar transporter proteins (STP). As a consequence, it is recommended that the symbols under the VviHTXX format should be kept as synonyms and the main symbol should be under the VviSTPXX format; the numbering of the genes should be in accordance with the phylogenetic tree performed in  [3]  as adapted in Figure  5B. The grapevine sugar transporter ERD6-like family was also compared to Arabidopsis  [3] ; the phylogenetic tree was adapted in Figure  5C. In this work, no symbols were assigned to the Arabidopsis genes, probably because they were never published, even though a nomenclature existed and they appeared as full names in the UniProt and NCBI databases. As a consequence no symbols were transferred to the Vitis genes in that publication.
The grapevine trihydroxystilbene synthase (STS) gene family was characterized in two concomitant articles As this family is not present in Arabidopsis, it is not possible to rely on sequence similarity with the Arabidopsis genes to address the nomenclature. While describing the genes, both authors used the same strategy to name the genes according to the syntenic positions, which is logical since the genes are grouped in two clusters on chromosomes 10 and 16. The names in both studies are identical. However, some of the genes were already described in previous studies  [50, 51] , and this was not taken into account for the naming of the members of the STS family. The genes were stored in public databases such as UniProt and Refseq under their original deno minations. The symbols are written differently, STS vs StSy, while the full names are both trihydroxystilbene synthase.
Platform for community curation of grapevine gene annotation The annotation platform for the grapevine genome is centralized and maintained in the ORCAE database with online interface from the VIB  [25]  and was chosen to perform community annotation for Vitis. ORCAE was developed with a gene-centric vision, meaning that the gene information pages are the central access points instead of a genome browser. The basic setup of ORCAE can be compared to a wiki system with information pages for each gene like a 'topic' page of a traditional wiki text. ORCAE was designed to suit the needs of genome sequencing projects from small consortia, like the grapevine. Like wikis, the data stored in ORCAE is never removed and a complete history of the changes applied by curators is kept. Also a number of analyses are run and updated in the background after changes affecting the gene structures have been supplied.
Updates to central repositories, like NCBI, will be organized on a six months basis, if the number of modifications can be considered as worthwhile. Users, willing to manually curate data will have to register with the ORCAE system, mostly to allow communication between curators worldwide. Also accounts are a way to remediate when erroneous modifications occur or to track errors in the input data, and discuss with the authors that mistakenly entered incorrect data. The whole systems history of modifications allows the retrieval of previous versions of gene models. Furthermore, to limit simple errors, tests have been implemented for checking the editing process, via the GenomeView application. These checks result in the ability of the system to reject genes models that contain obvious errors after user's modifications.
Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using a JTT model, and then selecting the topology with superior log likelihood value. The analysis involved 20 amino acid sequences. The coding data was translated assuming a Standard genetic code table. All positions containing gaps and missing data were eliminated. There were a total of 225 positions in the final dataset. Evolutionary analyses were conducted in MEGA5  [39]. Arrows point toward recommended Vitis symbols. Asterisks indicate redundant synonyms. also available  [52]  and will be updated with the present paper. Topics described in this tutorial are open to debate and be amended during the process of community annotation.
The sNCGGa can be contacted for enquiries at the Google group. One of the major goals is to bring together experts for each gene family to allow them to perform their annotation through the ORCAE annotation website, which in due time will synchronized with major public databases such as NCBI or Uniprot. The annotation should fit the IGGP Committee guidelines in terms of nomenclature and rules for addressing the level of confidence. In any case where possible, it is advised to annotate complete gene families or all the enzymes involved in a metabolic pathway, rather than a single isolated member of a larger group of genes.
Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using a JTT model, and then selecting the topology with superior log likelihood value. The analysis involved 40 amino acid sequences. The coding data was translated assuming a standard genetic code table. All positions with less than 95% site coverage were eliminated. That is, fewer than 5% alignment gaps, missing data, and ambiguous bases were allowed at any position. There were a total of 292 positions in the final dataset. Evolutionary analyses were conducted in MEGA5  [39]. Arrows point toward recommended Vitis symbols. A,B,C refer to the groups in  [4]. anticipate on the ever-growing amount of new sequencing data. It is important to consider that the collection of experimental evidence for grapevine genes will most likely be limited and that community is forced to opt for a strategy that can consider annotation inferred from similarity to other species.
Phylogenetic analysis Multiple sequence alignment was inferred using MUSCLE  [36]. The evolutionary history was inferred by using the Maximum Likelihood method based on the JTT matrixbased model  [37]. The bootstrap consensus tree inferred from 100 replicates  [38]  is taken to represent the evolutionary history of the taxa analyzed  [38]. Branches corresponding to partitions reproduced in less than 70% of bootstrap replicates were collapsed. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using a JTT model, and then selecting the topology with superior log likelihood value. The coding data was translated assuming a Standard genetic code table. All positions containing gaps and missing data were eliminated.
The analysis involved 40 amino acid sequences. The coding data was translated assuming a standard genetic code table. All positions with less than 95% site coverage were eliminated. That is, fewer than 5% alignment gaps, missing data, and ambiguous bases were allowed at any position. There were a total of 292 positions in the final dataset. Evolutionary analyses were conducted in MEGA5 [39]. Arrows point toward recommended Vitis symbols. A,B,C refer to the groups in [4].
Acknowledgements Authors would like to thank the  Grape Research Coordination Network  ( NSF grant   DBI 0741876 ) for financial support, the  International Grape Genome Program  and the  COST action FA1106  "Quality fruit". J. G. was supported by the  Ramon y Cajal program  ( RYC-2011-07791 ). We acknowledge support of the publication fee by the  CSIC Open Access Publication Support Initiative  through its  Unit of Information Resources for Research (URICI).
Methods 2. 1. The Dyn@mo Intervention. The Dyn@mo lifestyle intervention (Sainte-Justine University Hospital Center, Montreal, Canada) targets children and adolescents aged 6 to 17 years old with cardiometabolic risk factors, such as obesity, hypertension, disorders in glucose regulation, or dyslipidemia. Its primary goal is to promote physical activity and reduce sedentary time to improve children's cardiometabolic profile. To do so, the intervention relies on gathering data on mobility and physical activity using wearable sensors. These data provide a detailed picture of real-life conditions and physical activity levels, improving the health care professional's ability to tailor counseling. This paper presents these tools and baseline pilot data-that is, GPS and accelerometry collected for seven days after the first visit-among the first 37 patients who enrolled in the Dyn@mo program between March and November 2011.
The intervention comprises several clinical encounters and regular followup by phone and email. On three occasions (0, 12 months, and 24 months), children are equipped with a heart rate monitor, a GPS receiver, and an accelerometer to collect heart rate, daily mobility, and physical activity data during a 7-day period (results presented here use baseline, that is, data collected for seven days after first visit at 0 month). Four to six weeks later, patients, families, and professionals visualise resulting indicators which could be called a "spatio-behavioural diagnosis" using a map-based interactive web application. The next sections present the tools developed for such continuous monitoring and analysis of real-time data, including (i) the data collection tools and methods, (ii) the data treatment expert system, and (iii) the map-based interactive web application for rendering of spatio-behavioural information to patients and caregivers.
theactigraph. com/), and a Polar Wearlink Heart transmitter chest monitor with wireless link to the activity monitor receptor. The GPS device has a manufacturer-reported spatial accuracy of 1-3 meters and is configured to collect location information over an epoch of one second. Collected coordinates are saved in an ArcGIS shapefile (. shp) format by a coding procedure that was developed under ArcPad and installed on the Trimble device within the Windows Mobile 6 platform. Shapefiles are compressed and stored on local memory every hour. The ActiTrainer accelerometer is configured to record counts for each axis at a one minute epoch. The HR monitor records heart beats per minute. Data are saved on the local memories of the GPS device for GPS tracking and of the physical activity monitor for HR and accelerometry.
Upon reception of the devices at the clinic, data files are uploaded on a desktop computer for further processing. An ArcGIS ArcToolBox was developed using Python language to automatise data treatment, that is, to derive indicators from raw GPS, accelerometry, and heart rate data files and generate a synthetic XML output file for web integration. The XML file contains data to be visualised through the online application, including geographic information of activity locations  [8] , GPS tracks of trips, accelerometry-related information, and HR data. The procedure is automatedan analyst runs the ArcToolBox on the raw data, which generates the XML file. Further qualification of detected activity locations can be made through the web application. Raw data files are validated and cleaned prior to indicator construction.
Map-Based Interactive Web Application for Counseling. A web-based application supports data handling, data visualisation, and tailored counseling. Developed in a html and Flash programming environment, it runs on a IIS 7. 0 web server with a Microsoft SQL Server 2008 database deployed on the hospital's internal servers. After login, a caregiver has access to his/her patient's files. An administration page provides information on patient's intervention in terms of upcoming, planned, or completed meetings, advancement of data collection and upload, state of activity profile, and counseling reports. A web form allows registration of basic information including home and school address. Maps of the home and school areas are automatically generated, to facilitate validation of locations by participants.
A complementary mapping module documents relevant activity locations, such as actual places used for physical activity, or potential activity places, that is, sport centers, community centers, outdoor recreational areas, friend's homes, and so forth. The interactive map allows searching destinations through a textual search box connected to the Google Map API which will suggest results, or directly through the placement of a marker on the map. An infowindow allows collecting qualitative information for a documented location. This allows the mapping of "opportunities" for physical activity which can be enriched by contributions of the child, the family, and the caregiver. Expert-system generated indicators of physical activity, spatial information or combinations thereof are visualised through an interactive module through tables, graphs, or maps (see example of visual presentation of patient's residential neighbourhood and opportunities in Figure  1  and additional graphs and maps in Figure  2 ).
Participants and Measurements. The discussed tools and methods are illustrated using baseline data from the 37 first participants aged 6 to 17 who were enrolled in the program between March and November 2011. All patients had a specific cardiovascular risk factors (e. g. , diabetes, hypercholesterolemia, hypertension, obesity, etc. ). Patients and parents signed an informed consent authorising data use for research purposes. The Dyn@mo intervention and its related research program were approved by the Sainte-Justine hospital ethics committee. Physical activity and spatio-behavioural indicators were compiled and compared between primary school-aged and secondary school-aged children. Residential neighbourhood variables were further compiled within a Geographic Information using a 500 meter network-buffer centered on place of residence.
Results Summary statistics are provided in Table  1. Among the 37 initial participants, three had abnormally high accelerometryderived step counts (above 55,000 steps a day in average) and were considered as outliers and removed from accelerometry analysis. Among the 34 remaining patients, 24 were females, and 10 males. Average standardised Body Mass Index (zBMI according to WHO growth curves) was 3. 16 (SD = 0. 711). BMI for age classified 33 out of 34 patients as obese (i. e. , above the 95th percentile) and one patient as overweight (between the 85th and 95th percentile). 3. 1. Device Usage/Performance. Days with at least 10 hours of valid accelerometry measures were retained. Three participants provided less than 4 valid days, 27 participants 5 days or more, 22 participants 6 days or more, and 11 participants provided valid accelerometry data for the full seven-day period.
On average, a valid day included a total of 12 hours 54 minutes of wear time (SD = 59 minutes). Among the 34 participants, 33 had GPS data. Valid interpolated GPS data covered the full seven-day period for some 18 participants, 6 days for 3 participants, 5 days for 4 participants, and less than four days for three others. After data correction and imputation, cumulated GPS data was available on average for 17 hours and 10 minutes per day. GPS data was mostly imputed while being at home or at school (72%). Heart rate data was available for 34 participants. Only one participant provided less than 4 valid days, 31 participants 5 days or more, 27 participants 6 days or more, and 24 participants provided heart rate data for the full seven-day period. When establishing the convex hull polygon encompassing all GPS data points collected in a day-a measure of the area covered by a participant through his/her daily travelling-the median activity space area was 18.
Limitations. A series of limitations apply to the tools and methods used to support the Dyn@mo lifestyle intervention. The analysis and interpretation of accelerometry data to evaluate active living and physical activity require further refinement. We used cut-off points recommended by the Actilife 4. 3 User's Manual  [17] , yet, the distinction between nonwear time and sedentary time, the derivation of step counts, or transformation of accelerometry counts into levels of physical activity or energy expenditure estimates-that is, in part, establishing valid age-and BMI-specific cutoff points-remain contentious issues in pediatrics. Recent proposals have been made for nonwear/sedentary wearing time and could be tested  [18] , and comparative analyses have assessed the performance of different cut-off points and predictive equations of energy expenditure among children and youth  [19].
However, although pediatric obesity is on the rise and a major concern, relatively few accelerometry validation studies have been done among obese children  [20] [21] [22]. Concerning the use of a GPS device, issues related to battery life, manipulation errors, or device limitation such as nonfix or imprecision in measurement, are well described  [23, 24]. However, statistics showed good wearing times and satisfying collection of GPS tracks. Another limitation pertains to the use of the chest-mounted heart rate monitor. Data analysis clearly revealed measurement errors, with abnormal values-nonlife supporting-beat per minutes recorded among some participants. Adequate wearing of such devices may be problematic for longer periods and in free-living environments. For example, it is recommended to wet the electrode area of the chest belt for proper functioning.