dataesr/scientific-paragraphs-categorization huggingface dataset:
99 ourteen potential PKSs were identified as having this domain (Table  S3 , ESI †): six sequences encode multiple domains indicative of type I PKSs; three sequences encode one catalytic domain and an acyl carrier protein domain; one sequence carries two catalytic domains; four sequences only encode individual domains. The latter may be true type II PKS modules, or fragments of type I caused by failure to assemble full length transcripts. They may also be enzymes with other functions that this search technique reveals, such as fatty acid synthases. The poor Kozak consensus and lack of upstream stop codons indicate these genes could encode fragments of longer proteins. Attempts to predict the structures of the compounds synthesised by these putative Euglena PKS enzymes, based on domain architectures, using SBSPKS  100  and the PKS/NRPS Analysis Web-site,  101  were not successful.
This is probably due to the evolutionary distance from the bacterial and fungal species these pieces of software were designed to deal with. Analysis of the domain sequences of these enzymes using DELTA-BLAST, has allowed some assessment of the production line-like machinery and the products that could be made (Fig. 8 ). For example, in addition to a fully reducing PKS module, the largest polyketide synthase encoded in the Euglena transcriptome contains two enoyl hydratases and an HMGCoA synthase. As single domain proteins, these have been characterised in bacterial gene clusters as adding a b-methyl branch to polyketides such as bacillaene. 102 This novel domain architecture suggests the formation of an alkane followed by addition of a b-methyl branch. Thus, although Euglena is not known to make polyketides, there is apparent capacity for the synthesis of complex natural products of this type.
8 ), and the remainder have either only an A or only a C domain. Notably, three adenylation domain-encoding transcripts also have ankyrin domains, potentially involved in protein-protein interactions,  105  which may imply formation of non-covalent assemblies of NRPS modules. The function prediction programs, SBSPKS  100  and NRPS/PKS Analysis Website,  101  were unable to predict structure from these sequences. However, given the ability of E. gracilis to actively uptake iron from growth medium (Fig. S7 , ESI †), it may be reasonable to speculate that Euglena use NRPSs to produce peptide-based siderophores, a well-known strategy amongst the bacteria. 106.
RNA extraction, sequencing and bioinformatics Total RNA was isolated from 10 6 dark and light grown cells using RNeasy Minikit (Qiagen) and stored at À80 1C. Source Bioscience (Nottingham, UK) then performed mRNA purification, library preparation and sequencing on an Illumina HiSeq 2000 platform. A total of 313 205 944 and 137 689 062 100-base, paired-end reads were obtained from the light-and dark-grown samples respectively. The light-grown sequence data, downsampled to 100 million pairs of reads, and the entire dataset of 68 844 531 read-pairs from the dark-grown sample were used for separate de novo assemblies using Trinity 30 version r2013-02-25, executed in parallel on two 256 GB cluster nodes. 233 748 and 231 176 raw assemblies were generated from light and dark respectively. Likely coding sequences were extracted from these reads using the Perl utility script supplied with the Trinity distribution, transcripts_to_best_scoring_ORFs.
pl, producing 45 126 and 47 607 candidate ORFs of lengths greater than 100 amino acids. Putative coding sequences were combined and a non-redundant set of 32 128 peptides produced using CD-HIT 108 (v4. 5. 4) with an identity threshold of 0. 95 and a word length of 5. For functional annotation, the set was queried against the UniRef 100 protein database using BLASTP with an E-value threshold of 1 Â 10 À10. Sequence identifiers of the best hits were harvested and used to programmatically collect from databases, via SOAP-based web services, GO terms and KEGG pathway objects, and also used to enumerate kingdom-level taxonomic classifications for the species of origin. Transcript levels for isoforms were estimated from the reads using the RSEM wrapper script supplied in the Trinity distribution to implement the RNA-Seq by Expectation Maximisation methods.
Annotation of carbohydrate-active enzymes (CAZys) Putative carbohydrate-active enzymes were identified and classified using the methods used for updating the carbohydrate-active enzymes database (CAZy; www. cazy. org) as described previously. 40 riefly, the translated protein sequences of Euglena were compared to the full-length sequences derived from CAZy using BLAST. 110 The sequences that gave an E-value o 0. 1 were then subjected to a second BLAST search against a library made with the constitutive modules of glycoside hydrolases (GH), glycosyltransferases (GT), polysaccharide lyases (PL) and carbohydrate esterases (CE) and their associated carbohydrate-binding (CBM) modules. In parallel the sequences were subjected to a HMMer search using hidden Markov models built for each CAZy module family.
Acknowledgements These studies were supported by  Basic Technology  Grant  GR/ S79268/02  from  Research Councils UK , the  UK BBSRC Institute Strategic Programme Grant on Understanding and Exploiting Metabolism (MET)  [ BB/J004561/1 ], the  John Innes Foundation  and  NIES/NSF  [ R01 ES21968-1  for PVZ]. We thank  Tom Turner  for assistance with RNA preparation and  Mark Field  for advice on protozoan biology.
Comparison of results with different sets of data Results from the study period were compared with two collections of data, described below. i) Historical set of data: 5,140 fecal specimens were collected in the two years before the study period and had been manually plated in a three-quadrant method by experienced technical personnel (manual loop-to-plate method [10 µL]). ii) Official data from the German health authority: microbiological laboratories in Germany are obliged to notify cultures of Salmonella species, Shigella species, Campylobacter species, and Yersinia species immediately. Data are collected at the Robert Koch Institut, Berlin, Germany, and can be queried at http:// www3. rki. de/SurvStat. Furthermore, data are transmitted to the European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden.
The resulting sequences were assigned to the NCBI taxonomy using MEGAN software  (Huson et al. , 2007). Bacterial taxon assignments were largely in agreement with previous studies of the Drosophila microbiota, with members of the Lactobacillales (on average 14%) and Enterobacteriales (8%) orders and the Acetobacteriaceae (23%) family being the most abundant (Figure  S1 ). The Drosophila endosymbiont Wolbachia was also present at low abundance in all of our samples (Figure  S1 ). However, our analysis also identified 21 additional taxa at an abundance equal to, or higher than Wolbachia (Figure  S1 ), each of which was represented by only 0. 1-5% of the total bacterial sequences but which together made up, on average, 36% of the total bacterial sequence assignments. A large number of further taxa were represented by a very low number of read assignments, that nevertheless made up, on average, 10% of the total bacterial reads.
Increased levels of reactive oxygen species in the gut have been linked to the induction of epithelial renewal in response to infection and the commensal microbiota, via activation of the JAK-STAT pathway  (Buchon et al. , 2009). We therefore assayed expression levels of the JAK-STAT cytokine unpaired 3 (upd3) and target gene Socs36E, in post-Smurf intestines. Both upd3 and Socs36E showed a transient peak in expression 24 hours following the loss of barrier function (Figure  6B ). These data demonstrate a transient induction of epithelial renewal pathways and extended activation of immune signals in the post-Smurf intestinal epithelium. Previously, we reported that non-Smurf individuals do not show whole body increases in immunity gene expression during aging  (Rera et al. , 2012) , suggesting that age-related systemic immune activation is dependent upon barrier dysfunction.
Sequencing and Analysis Indexed paired end libraries were generated from genomic DNA extractions of dissected intestines using Nextera XT DNA sample preparation kit and Nextera XT Index kit (Illumina), with 1 ng of starting material and following the manufacturers' protocol, but with an extended fragmentation time of 8 minutes. Concentration normalized libraries were then pooled and sequenced on Illumina HiSeq2000 with TruSeq Dual Index Sequencing Primer Kit, Single Read (Illumina). The quality of the sequence dataset was confirmed using fastQC software (Babraham bioinformatics). Over 10,000,000 reads from each sample were then searched against a 2014 version of the NCBI non-redundant database (nr) using RAPSearch version 2. 18  (Zhao et al. , 2012) , limiting output to 50 matches per query.
Acknowledgements We thank  H. Jasper ,  L. Jones ,  L. Seroude ,  M. Dionne  and the  Drosophila Stock Center (Bloomington)  for fly stocks, and the L. Jones,  M. Frye  and  D. Simmons  labs for use of their equipment. This work was supported by the  UCLA Institute of Genomics and Proteomics  (M. P. and S. F-G. ) and the  National Institutes of Health  ( R01GM095656 , M. M. ; R24GM092473, S. F-G. ;  R00AG030493  and  R01AG045036 , W. W. J. ;  R01AG037514 ,  R01AG049157  and  R01AG040288 , D. W. W. ).
Indeed, fulfillment urban distribution is subject to unexpected incidents that happen during execution of the delivery plans like order cancellation, new customer requests, changes of delivery order and/or destination, mechanical failures, and so on  (Zeimpekis, 2011). This seriously impacts warehouse planning and requires re-planning the whole or part of order fulfillment (pick/pack/ship). This also causes performance inefficiencies (additional handling costs, delay penalty costs, delay in delivery time). There is a significant lack of research related to city logistics warehouse management  (Crainic et al. , 2009; Morana et al. , 2014). Typical warehouse management processes are assured by application software packages such as Enterprise Resource Planning (ERP), Warehouse Management System (WMS), Transport Management System (TMS) and Advanced Planning and Scheduling (APS) software  (Helo and Szekely, 2005).
City hub application architecture Platforms associating MAS with RFID and ambient intelligence have been proposed in several research works  (Wang et al. , 2009; Bade, 2009; Olaru and Gratie, 2011). It consists of many layers such as application layer, network layer and physical devices layer. The city hub application architecture that we propose can be discussed in four different layers, as shown in Fig. 4. The physical devices layer consists of all the physical, embedded devices (i. e. pallets, forklifts, packing machines, trucks) that are equipped with RFID readers and sensors. These physical objects store information on both RFID tags and on remote databases. The device discovery protocol is selected to implement the UPnP (Universal Plug and Play) middleware layer that then interfaces with the devices.
HISTORY AND KEY DATA OF THE REALCAT PROJECT In this context, a first version of what would become later the REALCAT 1  platform has been imagined in 2010. After some modifications in 2011, the project was accepted in December 2011 and funded by the French Government in the frame of the EQUIPEX part of the 'Future Investments' program (PIA), with the contractual reference 'ANR-11-EQPX-0037'. The global budget represents 8. 7 M1 over 10 years (including 6. 5 M1 of equipment) plus 700 k1 from the Nord-Pas-de-Calais Region in the frame of the FEDER program and 10 k1 from the Centrale Initiatives Foundation. The French National Research Agency (ANR) is in charge of the administration of the REALCAT project. The project gathers four laboratories, all located on the same campus in Villeneuve d'Ascq (North of France).
Introduction At full capacity, the platform aims at running 20 projects per year involving worldwide external users from both academic and industrial areas. Among these 20 projects, it is estimated that half of them will be performed in the frame of industrial collaborative projects; 20% in the frame of academic collaborative projects (including the projects led by the members of the REALCAT consortium only), and 30% of provisions of services. A separate office is available close to the REALCAT lab to welcome the external users. Confidentiality and safety are keywords for the REAL-CAT project. Important measures have been taken to ensure that all the projects will be conducted in safe conditions for the operators and that the data transmission will be strictly limited to the authorized persons.
For instance, the treatment of the huge amount of data generated by the HT instruments will be a challenge. No doubt that the input of the LIFL and LAGIS labs in this domain will be very helpful. In the same area, the set-up of a Laboratory Integrated Management System (LIMS) in the next months, including database gathering the information issued from a given sample from all the instruments of the platform, will surely strongly reinforce the possibilities of interpretation of the data collected on REALCAT. Within the frame of the REALCAT project, methodological developments will also be carried out in partnership with private companies in order to develop innovative HT tools for catalysis not yet available on the market. This will ensure that REALCAT remains the most advanced HT platform for catalysis in the world in the future.
ACKNOWLEDGMENTS The REALCAT platform is benefiting from a Governmental subvention administrated by the French National Research Agency (ANR) within the frame of the 'Future Investments' program (PIA), with the contractual reference 'ANR-11-EQPX-0037'. The Nord-Pas-de-Calais Region and the FEDER as well as the Centrale Initiatives Foundation are thanked for their financial contribution to the acquisition of the equipment of the platform. The Université Lille 1 Sciences & Technologies (USTL) is also acknowledged for having financed a part of the consulting fees for designing the program and the École Centrale de Lille (ECLille) is warmly thanks for their financial contribution necessary for the setup of the implantation laboratories. The consulting was entrusted to the ALMA Consulting Group, and we would like to warmly thank Dr.
DNA methylation and mRNA expression data DNA methylation status of tumor tissue and normal lung tissue was established using Illumina Infinium HumanMethyla-tion450 BeadChips that cover 485,764 cytosine positions of the human genome. Preprocessing and normalization involved steps of probe filtering, color bias correction, background subtraction and subset quantile normalization as previously described  (Touleimat and Tost, 2012). After preprocessing of the data 456,946 probes and 164 tumor samples and 19 normal lung samples were included. The bioinformatic analyses were performed using two different datasets. The first dataset included methylation levels of all probes that remained after the preprocessing step. The second dataset, the "gene region collapsed" methylation dataset, was constructed to reduce the dimensions of the methylation data and to focus the analyses on regions that are most relevant for gene function.
In this dataset, we used the median methylation levels of all CpGs mapped to one of the six subregions in the gene. The subregions are located 1) between 1500 and 200 base pairs upstream of transcription start site (TSS), 2) between 200 bp upstream of TSS to the TSS, 3) in the 5 0 UTR, 4) in the first exon, 5) in the gene body or 6) in the 3 0 UTR. The intergenic CpGs were not included in this dataset. The "gene region collapsed" dataset included 79,000 targets. The raw data and normalized data are available in Gene Expression Omnibus (GEO) with accession number GSE66836. Technical validation of the methylation data was performed by pyrosequencing of 11 selected probes in 162 tumor samples and 18 normal lung tissue samples (Supplementary file). A subset of the lung adenocarcinoma samples (n ¼ 121) had mRNA expression data available.
The same steps as above were performed on the "gene region collapsed" methylation dataset. SAM analyses were applied to study differentially methylated CpGs between tumor tissue from never-smokers and smokers/former smokers, and between mutated and wild type tumors of the EGFR, KRAS and TP53 genes. To identify differentially methylated gene regions between the identified clusters, multiclass SAM was applied, and the significant gene regions were associated with the different clusters by identifying the cluster with the median methylation level furthest from the other clusters (calculated with subtraction of the median methylation level for each cluster from the median methylation levels across all clusters). Methylation differences with FDR q-value smaller than 0. 01 (1%) and difference between the median methylation levels in the two groups more than 0.
Ingenuity Pathway Analysis The data was analyzed using Ingenuity Pathway Analysis (IPA, http://www. ingenuity. com/). Core analyses were assessed to find the level of representation of our selected genes in already defined canonical pathways. The significance of the association between our defined gene lists and the molecular functions, diseases and canonical pathways are calculated using Fisher's exact test and the association with the canonical pathways are corrected for multiple testing by using a Benjamini-Hochberg approach.
The patients were divided into two groups based on the mean prognostic index; those with prognostic index below the mean (low index) and those with prognostic index above the mean (high index). The two groups were tested with log-rank analysis and KaplaneMeier curves. Log-rank tests were performed to identify clinicopathological factors (sex, stage, age at surgery, adjuvant chemotherapy, smoking status and EGFR-, KRAS-and TP53 mutational status) with significant influence on progressionfree survival using the functions Surv, survfit and survdiff (R package survival  (Therneau T, n. d. ) ). A Cox regression model was applied for multivariate survival analysis using the function coxph (R package survival  (Therneau T, n. d. ) ). Factors included in the multivariate model were stage, age at surgery, adjuvant chemotherapy, smoking status and the prognostic index.
Results Genome-wide DNA methylation profiles using the Illumina Infinium 450K array were obtained for 164 lung adenocarcinoma samples and 19 matched normal lung tissue samples. The tumor samples were tested for mutations in the EGFR-, KRAS-and TP53 genes and 20 tumors harbored EGFR mutations (12. 4%), KRAS and TP53 mutations were found in 53 (34. 4%) and 58 (38. 4%) tumors, respectively. All of the patients with EGFR-mutated tumors were never or former smokers while only 3 patients with KRAS-mutated tumors and 8 patients with tumors with mutation in the TP53 gene were never-smokers. EGFR-and KRAS-mutations were mutually exclusive. Nine tumors had both EGFR mutation and mutation in the TP53 gene and 12 of the KRAS-mutated tumors also contained a TP53 mutation. Pyrosequencing was performed as technical validation, and this data showed good concordance with the array results especially when the difference between the median methylation levels between groups were more than 20% (Supplementary Table  1 ).
SAM was also applied to the "gene region collapsed" methylation dataset comparing the differentially methylated gene regions between the lung adenocarcinoma tissue and the normal lung tissue. This analysis revealed 1222 differentially methylated gene regions, representing 1120 unique genes. 718 gene regions were hypermethylated (representing 666 unique genes) and 504 gene regions were hypomethylated (representing 467 unique genes). Both the hypermethylated and hypomethylated loci were distributed in all gene regions (Figure  2 ). Some of the hypermethylated CpGs in tumor tissue were localized in cancer related genes like Transforming growth factor ß (TGFß) and Insuline-like Growth Factor 2(IGF2) and some have previously been reported as hypermethylated in lung cancer tissue such as the HOX gene clusters  (Lokk et al.
05, representing 1070 unique genes. In this analysis the methylation levels of 2347 CpGs (435 unique genes) were observed with a negative correlation and 2118 CpGs (716 unique genes) were observed with a positive correlation. The negative correlations were located at all distances between À100 kb and þ100 kb from TSS, but were enriched close to the TSS. The positive correlations were evenly distributed across the whole area analyzed (Supplementary Figure  2 ). Figure  3  e Significant correlations between the median DNA methylation levels of gene regions and mRNA expression. This plot shows both the negative and positive correlations and location of the methylated regions. This plot is based on the results from the "gene region collapsed" dataset.
Prognostic index In order to identify a smaller number of CpGs associated with time to progression a Lasso approach was applied. The dataset was filtered by correlation with mRNA expression and variation in the methylation levels so that 76,478 probes remained in the analysis. Using this method we identified a signature of 33 CpGs associated with time to progression (Table  2 ). The prognostic index for each patient was based on the estimated coefficients (X) from the signature and the methylation values of the 33 probes in each lung adenocarcinoma sample. Using a log-rank test, we identified that the patients with low prognostic index had a significant better prognosis than the patients with a high prognostic index (p-value < 2. 2 Â 10 À16 ; Figure  4A ). Univariate survival analysis was performed on different clinical variables.
Data analysis The data were analyzed using FieldTrip  (Oostenveld et al. , 2011; version 20130713 ) and custom written Matlab code. The MEG data was denoised by computing noise cancelation weights using three reference channels. First, PCA was computed on the reference channels and then projected to the gradiometer channels and subtracted from the data of interest. This computation was performed separately for each trial using FieldTrip's ft_denoise_pca function. A 50 Hz notch filter and a bandpass FIR filter (0. 01-130 Hz) were applied to the EEG and MEG data. Trials containing excessive ocular artifacts, movement artifacts, amplifier saturation, or SQUID artifacts were selected by visual inspection and excluded from further processing. Eye blinks, heart beat, and muscle artifacts were corrected using Independent Component Analysis  (Bell and Sejnowski, 1995).
The time courses of the sensors different from the baseline interval can be seen in the upper-right panel of Fig. 5 , with the time courses depicting RMS across the MEG sensors significantly different from the baseline interval (note that all significant sensors, without performing cluster correction, were selected to better depict the time courses of magnetic fields). Indeed, the RI RL plots show a clear buildup that starts around 0. 6 s before the response that terminates reproduction interval. Earlier work has linked this premovement magnetic field (preMMF) with preparation-based processes  (Cheyne et al. , 2006). For better topographical visualization and to determine the source of the observed effects, we reconstructed sources for the data analyzed for RI SL and RI RL. Fig. 6  shows the source reconstructions obtained using LCMV beamformer for both RI SL and Fig.