dataesr/scientific-paragraphs-categorization huggingface dataset:
, ODON final report). In OPEC, it was found that changing sampling frequency from weekly to daily of a ferrybox line in the Aegean Sea can increase the explained chl a variability from 35 to 96. 5 %. In order to build up a quality assured European capacity to deliver the OE service, an "Operational Ecology European Experiment -OEEE" is required. This would serve as part of the mid-to long-term research element of European OE. The goal of the OEEE is to integrate as many as possible existing observations and advanced modelling technologies to develop and demonstrate OE showcases in European regional seas. This can be reached through six research activities: to establish a comprehensive database by integrating existing European marine monitoring components (as described in Sect. 2) for testbed studies, to understand the ecosystem model behaviour in a probabilistic framework, aiming at generating unbiased ensembles (regarding to ecosystem reality) for the dedicated model system, to improve the quality of the forcing data from atmosphere deposits, riverine inputs and physical ocean, as well as better description and parameterisation of the forcing terms, to generate OE products to assimilate as much as possible observations into the improved model system in an ensemble framework.
DNA and Protein Sequence Analysis The barley ATG, ATI1 and NBR1 genes were identified using homologous protein sequences from Arabidopsis and rice (Oryza sativa) as queries. The Arabidopsis and rice ATG gene and protein sequences were obtained from The Arabidopsis Information Resource (TAIR) (http://www. arabidopsis. org/) and GRAMENE (http://www. gramene. org/) databases (Supplementary Table  1 ; Supplementary Figure  1 ). The Arabidopsis and rice protein sequences were then used to search by TBLASTN and TBLASTX  [27]  in the following cDNA, EST, and BAC (bacterial artificial chromosome) clone libraries as well as protein and genomic assembly databases: GeneBank, http://www. ncbi. nlm. nih. gov/; European Nucleotide Archive (ENA), http://www. ebi. ac. uk/ena/; UniProt, http://www. uniprot.
org/; European Bioinformatic Institute (EBI), http://www. ebi. ac. uk/ (last searched November 2013). The identified HvATG cDNA sequences were then used as BLASTN query sequences against the full barley genome sequence found in the EnsemblPlants (http://plants. ensembl. org/) database in order to obtain the gene models and to assign each cDNA to a genomic locus. All the barley HvATG genes were found through BLAST searches using both coding DNA sequences (CDSs) and predicted protein sequences of the three species: Saccharomyces cerevisiae, Arabidopsis thaliana and Oriza sativa. Only sequences with the lower e-values were considered as homologues for all ATG genes described in this work (Figure  1  and Table  1 ). Only one barley homologue gene is described for each ATG gene in this work (Figure  1  and Table  1 ).
Sequence alignments between HvATG and ATG protein sequences of other species were also manually inspected for conserved and essential amino acids. Alignments were performed on the whole protein sequence. These protein sequences were also used to perform phylogenetic analysis. The ATG protein sequences for other species different to Arabidopsis and rice were found through the GeneBank database searches. The multiple protein sequence alignments and the phylogenetic trees were generated using the ClustalW algorithm and the Phylogeny MABL program available online (http://www. phylogeny. fr/simple_phylogeny. cgi;  [28] ). Figures of the HvATG gene model were generated by Exon-Intron graphic maker (http://wormweb. org/exonintron). Splice variants predicted by the EnsemblPlants database and the corresponding accession numbers are found in Table  1.
5 ˝C every 30 s. Fluorescence readings were taken during the elongation step (72 ˝C). Ct values were calculated by the CFX connect software. Genes and primers are listed in Supplementary Table  2. Several reference genes (including GADPH, Actin, SAMd, CHS90, α-Tubulin, β-Tubulin, EF1a, ADPrf1, CDC48 and Ubiquitin) were trialed and only GADPH was validated across all samples under nitrate limitation and dark stress assays and Actin was validated across all senescence stages in samples from field experiments in accordance with the geNorm algorithm  [29]. Results are shown as Log 10 fold changes in the transcript levels of samples compared to the youngest leaf.
Identification of ATG, NBR1 and ATI Genes in Barley In order to find the barley HvATGs, HvNBR1 and HvATI homologue sequences, we used the rice (OsATG, OsATI and OsNBR1), Arabidopsis (AtATG, AtATI and AtNBR1) and yeast (ScATG) equivalents as queries. Yeast equivalents are single genes for each function, while in Arabidopsis and rice several ATG functions are encoded by gene families. This is the case for ATG1 (three genes in Arabidopsis and four in rice), ATG3 (two genes in rice), ATG4 (two genes in both Arabidopsis and rice), ATG8 (nine genes in Arabidopsis and five genes in rice), ATG9 and ATG10 (two genes of each in rice), ATG12 (two genes in Arabidopsis and three genes in rice), ATG13 (two genes in both Arabidopsis and rice) and ATG18 (eight genes in Arabidopsis and six genes in rice). The Arabidopsis, rice and yeast ATG collections (Supplementary Table  1 ) were used as queries in the BLASTX and TBLASTN searches in the different DNA and protein sequence databases including cDNA, EST (e.
g. , GeneBank and ENA), the genomic assembly databases (e. g. , EnsemblGenomes) and the protein sequence database (e. g. , Uniprot, EBI). Gaps in the sequences were eliminated by sequence analysis of the corresponding bacterial artificial chromosome clone sequences available in the databases previously mentioned. Finally, all the cDNA sequences found were aligned with genomic sequences in order to establish the HvATGs, HvATI1 and HvNBR1 gene models and reciprocal blasts were performed to confirm results (Figure  1 ). A total of twenty-four HvATG genes were found in our study (Table  1 ). Only one HvATI gene could be found. Single genes encode HvATG3, HvATG5, HvATG7, HvATG9, HvATG11, HvATG12, HvATG13, HvATG16, and HvATG101, while HvATG1, HvATG8 and HvATG18 are encoded by gene families (two genes for HvATG1, three genes for HvATG8 and five genes for HvATG18; Table  1  and Supplementary Data Set 1).
For many putative proteins, high amino acid sequence conservation was observed when they were compared with their homologues in rice, but there was less identity with their Arabidopsis and yeast equivalents. For example, HvATG5 has 72% identity with OsATG5, 48% with AtATG5 and 13% with ScATG5. Other proteins showed high identity with all counterparts; for example, HvATG8_1 has 87% identity with OsATG8a, 86% with AtATG8a and 71% with ScATG8a. This protein also shows high identity with other ATG8 proteins from organisms such as drosophila, mice and humans and represents one of the most conserved proteins of all HvATG collection (Figure  2 ). GeneBank and ENA), the genomic assembly databases (e. g. , EnsemblGenomes) and the protein sequence database (e. g. , Uniprot, EBI). Gaps in the sequences were eliminated by sequence analysis of the corresponding bacterial artificial chromosome clone sequences available in the databases previously mentioned.
No essential amino acid residues were found in the predicted proteins of the HvATG18 family. Due to the low identity of HvATG18_4 and HvATG18_5 amino acid sequences with the counterparts of other species and the fact that no key amino acids essential for their function could be found in these sequences, we cannot be assured that these genes encode for ATG functions in barley. Further studies are needed to confirm that these sequences have HvATG function. Our gene models (Figure  1 ) were performed by aligning the collected EST, BAC and cDNA sequences with the available genome sequences  [26]. We then compared our gene models to the ones published by the genome assembly database EnsemblPlants (http://plants. ensembl. org/index. html), (Supplementary Figure  4 ). EnsemblPlants predicted and proposed several splice variants for almost all the HvATG genes (Supplementary Figure  4 ), and apart from the case of HvATG5, for each of the other genes, only one of the EnsemblPlants proposed splice variant corresponded to our gene model.
No essential amino acid residues were found in the predicted proteins of the HvATG18 family. Due to the low identity of HvATG18_4 and HvATG18_5 amino acid sequences with the counterparts of other species and the fact that no key amino acids essential for their function could be found in these sequences, we cannot be assured that these genes encode for ATG functions in barley. Further studies are needed to confirm that these sequences have HvATG function. Our gene models (Figure  1 ) were performed by aligning the collected EST, BAC and cDNA sequences with the available genome sequences  [26]. We then compared our gene models to the ones published by the genome assembly database EnsemblPlants (http://plants. ensembl. org/ index. html), (Supplementary Figure  4 ). EnsemblPlants predicted and proposed several splice variants for almost all the HvATG genes (Supplementary Figure  4 ), and apart from the case of HvATG5, for each of the other genes, only one of the EnsemblPlants proposed splice variant corresponded to our gene model.
The use of rice (OsATG), Arabidopsis (AtATG) and yeast (ScATG) ATGs as queries was sufficient to optimize the number of genes found. In addition, by proceeding in this way we ensured that all of the 24 HvATG genes described in this report (Figure  1 ) are translated into mRNA. We verified that the proteins encoded share all the characteristics and essential amino acids known from studies performed on other ATG gene homologues in plants, animals and yeast. In addition, starting from the cDNA clones, we identified a new splice variant of HvATG5 that was not proposed by the genome assembly database, EnsemblPlants (http://plants. ensembl. org/index. html). In the case of ATG5, which is highly important for the autophagy machinery in both plants and animals  [10] , the 5 1 sequence region of the HvATG5 coding sequence was missing from the genome sequence, and this was likely due to the difficulty of sequencing the high GC content of this 5 1 end.
Acknowledgments: The authors thank  Joël Talbotec  ( INRA Versailles, France ) for taking care of the plants,  Pr Karin Krupinska  ( University of Kiel, Kiel, Germany ) for facilitating plant material harvest during the field experiment,  Kohki Yoshimoto  ( INRA Versailles, France ) for advice, and  Laurence Cantrill  for correcting English.
3482T>C; p. Met1161Thr and c. 4709C>A; p. Thr1570Lys. A search for SCN10A variants in three consortia datasets (EuroEPINOMICS, Epi4K/EPGP, Autism/dbGaP) identified an additional five individuals with compound heterozygous variants. A Hispanic male with infantile spasms [c. 2842G>C; p. Val948Leu and c. 1453C>T; p. Arg485Cys], and a Caucasian female with Lennox-Gastaut syndrome [c. 1529C>T; p. Pro510Leu and c. 4984G>A; p. Gly1662Ser] in the epilepsy databases and three in the autism databases with [c. 4009T>A; p. Ser1337Thr and c. 1141A>G; p. Ile381Val], [c. 2972C>T; p. Pro991Leu and c. 2470C>T; p. His824Tyr], and [c. 4009T>A; p. Ser1337Thr and c. 2052G>A; p. Met684Ile]. Interpretation: SCN10A is a member of the voltage-gated sodium channel (VGSC) gene family. Sodium channels are responsible for the instigation and proliferation of action potentials in central and peripheral nervous systems.
Material and Methods The study was conducted in accordance with the provisions of the Declaration of Helsinki and a signed informed consent was obtained from each participant or his/her legal guardian. Blood samples from all available family members of the three families were obtained and genomic DNA was extracted with Gentra Puregene Blood kit [Qiagen, Valencia, CA] according to the manufacturer's specifications and guidelines. Homozygosity mapping was performed utilizing the Human Mapping OmniExpress-12 v1-1 SNP genotyping array [Illumina, San Diego, CA]. Data iltration and genotyping were done with GenomeStudio v2011. 1. For the determination of linkage intervals, data were analyzed by the "HomozygosityMapper" software. 1, 2 or families 1 and 2, whole exome target enrichment was performed on ABI SOLiD TM 4 platform (Applied Biosystems, Foster City, CA) according to the manufacturer's specifications.
DNA library preparation was with TargetSeq TM Exome Enrichment system (Applied Biosystems) as multiplex fragment libraries utilizing both the SOLiD TM Fragment Library construction Kits and SOLiD TM Fragment Library Barcoding Kit Module 1-16 for the ABI SOLiD TM 4 system. Bead preparation and enriching was done on an EZ Bead Emulsifier, Amplifier and Enricher utilizing E80 scale (Applied Biosystems). Sequencing modality was with multiplex fragment paired-end. The bioinformatics of the whole exome NGS data started with the analysis of the raw data files (in a proprietary XSQ format) with the Life Technologies LifeScope v2. 5. 4 software (Life Technologies, Carlsbad, CA) on a dedicated cluster to align the reads produced by the SOLiD TM to the hg19 reference sequence, sourced from the University of California at Santa Cruz Genome Informatics Group (UCSC).
The aligned (BAM) files were validated, duplicate sequences were identified and removed and incorrectly identified Mate-Pairs were corrected, using the Picard v1. 87 software. The Genome Analysis Tool Kit (GATK) v. 3. 0. 0 (Boston, MA, USA) was applied to the "corrected" output files to recalibrate the base quality scores, using machine learning to model any systematic errors in the data, to carry out localized realignments around possible insertion/deletion sequences to ensure mapping accuracy, to identify viable variants from the sequence reads and to recalibrate the variants to ensure accuracy of the variant calling in a variant-specific manner. Once a suitable list of variants was produced, it was filtered using in-house scripts to confirm variant zygosity and identify those variants that conform to the inheritance model.
These variants were annotated using an in-house script in conjunction with the software, ANNOVAR [http://www. openbioinformatics. org/annovar/ ] to produce an annotated variant list with the most recent available information from a number of reference Websites. Whole Exome sequencing for Family 3 was performed on Illumina HiSeq2000 (Illumina) with 101-bp pairedend read sequencing. Image analyses and base calling were with the Illumina Genome Analyzer Pipeline software v. 1. 13. 48. 0 with default parameters. Reads were aligned to a human reference sequence (UCSC hg19; NCBI build 37) with the "Efficient Large-scale Alignment of Nucleotide Databases" software (Illumina). Genotypes were called at all positions with high-quality sequence bases using the Bayesian algorithm "Most Probable Genotype.
" The BAM files were visualized with Integrative Genomics Viewer (The Broad Institute, Cambridge, MA). Variants were filtered using VarSifter v1. 5 tool by considering minor allele frequency (MAF) of <0. 05 and the availability of variants in public databases of dbSNP, ClinSeq and EXAC (The Broad Institute). Filtered variants were annotated with CADD score using online tools [http://cadd. gs. washington. edu/score]. Targeted DNA resequencing was done by Sanger Bigdye terminator v3. 1 cycle sequencing (Applied Biosystems) on an ABI 3730 automatic sequencer (Applied Biosystems) to screen for mutations in candidate genes, perform allele frequency studies in related populations, and determine co-segregation of variants with the phenotype within each family. VCF files were obtained from the dbGaP entry for the ARRA Autism Sequencing Collaboration (phs000298).
The WES and comparative genome analyses for Family 3 showed no significant de novo, homozygous or Xlinked variants. Compound heterozygous exonic nonsynonymous variants were identified in SASH1: c. 2458C>T; p. Arg820Trp and c. 2995G>A; p. Gly999Arg, and in SCN10A: c. 3482T>C; p. Met1161Thr and c. 4709C>A; p. Thr1570Lys. Following the identification of the SCN10A variants in the three families, three consortia databases were examined for compound heterozygous variants in SCN10A. The Epi4K/EPGP dataset,  4   2. The positions of all protein variants are shown in Figure  2.
2 ), putatively in an extracellular position. Accordingly, the SCN10A variant was considered the best candidate gene responsible for the phenotype in this family. In Family 2, two exonic nonsynonymous homozygous variants contained within runs of homozygosity, namely LAMB2: c. 4981C>T; p. Arg1661Trp and SCN10A: c. 4735C>T; p. Arg1579*. Both variants co-segregated with the phenotype and were absent in 400 ethnically matched control chromosomes and publically available databases. However, the LAMB2 variant is predicted to be benign  9, 10  and affects a residue that is not evolutionary conserved. 8 Moreover, mutations in LAMB2 are associated autosomal recessive Pierson syndrome (MIM 609409) and nephrotic sydrome type 5, with or without ocular abnormalities (MIM 614199). 11 On the other hand, the SCN10A variant affects the voltage-sensor helix S4 in Domain IV (Fig.
further genotype-phenotype correlation would require identification of additional cases. Since other sodium channel genes, such as SCN1A and SCN2A have been associated with epilepsy,  23, 25  and SCN1A leading to Dravet syndrome is classically precipitated by fever, it is interesting that another refractory epileptic encephalopathy triggered by fever (FIRES) has now been associate with another sodium channel gene. 14 In addition, the expression of SCN10A in CNS neurons, as well as in the developing brain provides biological plausibility for the phenotype produced by the biallelic mutations. 38À41  Epileptic seizures affect 7-46% of children with ASD, especially those with intellectual developmental disorder and ASD occurs in 15-35% of those with epilepsy. 42 xamination of databases containing whole genome or whole exome data for patients with epilepsy, ASD or both for compound heterozygosity for mutations within genes identified through homozygosity studies would be quite useful.
In this study the identification of compound heterozygosity in two patients with LGS and infantile spasms and three patients with ASD further implicates SCN10A in these disorders. In conclusion, through an international collaboration, we have identified four patients with biallelic SCN10A variants who presented with epileptic seizures. The review of three different publically available disease databases has added several other patients, although the variants may not be responsible for the phenotype. Since epilepsy is observed along with intellectual impairment and peripheral neuropathy, biallelic SCN10A mutations likely interfere with normal functioning of this sodium channel, resulting in aberrant transmission of nerve impulses.
containing WES data on 356 patients, identified SCN10A compound heterozygous ª 2016 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals, Inc on behalf of American Neurological Association. variants in two patients; a Caucasian female with Lennox-Gastaut syndrome (LGS, MIM 606369) [c. 1529C>T; p. Pro510Leu and c. 4984G>A; p. Gly1662Ser] and a Hispanic male with infantile spasms [c. 2842G>C; p. Val948-Leu and c. 1453C>T; p. Arg485Cys]. The Autism dbGaP dataset, containing WES data from 486 patients with ASD, identified SCN10A compound heterozygous variants in three patients; [c. 4009T>A; p. Ser1337Thr and c. 1141A>G; p. Ile381Val], [c. 2972C>T; p. Pro991Leu and c. 2470C>T; p. His824Tyr], and [c. 4009T>A; p. Ser1337Thr and c. 2052G>A; p. Met684Ile]. The MAFs of all identified variants, the in silico mutation effect predictions and amino-acid conservation across species are presented in Table.
Acknowledgment This publication was made possible in part by  Qatar National Research Fund (QNRF) NPRP  grant  09-367-3-087  (MK and HE) and  NPRP  grant  6-359-3-095  (HE and AGB) and by the generous support of the  Shafallah Center Foundation, Doha, Qatar. It was also supported by  NIH   1R01 NS064159-01A1  (AGB) and  5R21MH100086-02  and the  National Human Genome Research Institute Intramural Research Program. The support for the Autism Sequencing Collaborative was provided by grants  R01-MH089208  awarded to  Mark Daly ,  R01-MH089175  awarded to  Richard Gibbs ,  R01-MH089025  awarded to  Joseph Buxbaum ,  R01-MH089004  awarded to  Gerard Schellenberg  and  R01-MH089482  awarded to  James Sutcliffe. Our appreciation to the  Epilepsy Epi4K consortium: Discovery in Epilepsy study  ( NINDS U01-NS077303 ) and the  Epilepsy Genome/Phenome Project  ( EPGP-NINDS U01-NS053998 ).
RELATED WORK The CSC Phase 1 project launched by the European Commission and ETSI 1 in 2012 delivered an overview of the Cloud standards landscape. The CSC Phase 1 project presented the results of the project in a report published in 2013  (ETSI, 2013). These results are used as groundwork for the CSC Phase 2 project. Moreover, during the last couple of years there have been a number of surveys with different objectives related to the usage and experiences in the Cloud Computing space. Most of them have been useroriented and targeted to identify areas where users see a need for improvement or missing standards, e. g. , the 2014 Trusted Cloud survey of the European Commission (ECTCES, 2014),  (ECTCESeval, 2014). In many different surveys, security, data protection, or Service Level Agreements are among the top ranked concerns related to Cloud Computing.
g. , inter-substation communication, outage management, DERs integration. Utilities can easily use the existing LTE infrastructure to significantly reduce the investment/deployment costs. LTE could potentially support remote automation services in the distribution grid given that its coverage extends to wide geographical regions. In addition to low latency, LTE offers high data rates and system reliability, enabling currently unrealizable critical control tasks. However, LTE was not initially designed for smart grid applications, but for human-centric communications. Smart grid data characteristics (sporadic nature, uplink-based) are different than those generated by broadband services originally thought for LTE, and hence LTE needs to be optimized for energy traffic flows. In this paper, we investigate up to which extent public LTE infrastructure can support real-time automation services in distribution grid.
We address this issue by prioritizing energy data flows over conventional LTE traffic in network-overload scenarios. In this context, the role of scheduler is significant to resolve the contention. In static allocation schemes  [6] , no resource contention is taking place; however, such schemes are rendered inefficient in terms of spectrum utilization. The downlink performance of LTE round-robin scheduler in network-underload was investigated in  [7]  for IEC 61850 MMS services. Instead, here we propose a channel-aware adaptive scheduling scheme to address the most challenging uplink scenario, where traffic overload naturally arises due to the scarcity of uplink resources  [8]. In  [9] , an LTE scheduler is analytically designed to maximize the control traffic rate. Instead, we propose an adaptive scheduling scheme that dynamically allocates contiguous uplink resources and prioritizes smart grid traffic to guarantee its strict latency and throughput requirements.