dataesr/scientific-paragraphs-categorization huggingface dataset:
langsethiae] FGRRES_07699 FGSG_07699 IV 4,231,666 4,232,190 Uncharacterized - - - protein FGRRES_09118 FGSG_09118 IV 7,533,260 7,536,888 Uncharacterized IPR001002, Chitin binding; related to chitinase protein IPR001223, chitinase II [Fusarium IPR011583, proliferatum] IPR013781, IPR017853, IPR018371, IPR029070.
Thereby, the 1201treated SIPS cell transcriptome seems closer to that of quiescent than to SIPS cells (Fig. 4c ). Raw data are publicly available at Gene Expression Omnibus (GEO) website (accession number GSE93535). Functional annotation clustering of mRNAs that were significantly changed by SIPS and reverted back by 1201 (Fig. 4d ) revealed a strong correlation with processes of the immune response among others (Supplementary Table  S1 ). Out of 89 published SASP factors,  5, 38, 39  29 were upregulated in SIPS HDFs (Fig. 4d  and Supplementary Table  S1 ), 7 of which were significantly downregulated by 1201 (Fig. 4d, e ). In addition, we included CXCL1, despite being not significantly upregulated by our SIPS treatment (p = 0. 13), due to its role as an important chemotactic and pro-tumorigenic SASP factor and its response to the treatment with 1201.
Taken together, we have identified a plant extract that is able to block detrimental effects of cellular senescence in HDFs and to maintain their functionality. Furthermore, our results give additional evidence for a connection between cellular senescence and PRT. We have also identified numerous putative novel SASP factors, highlighting that the list of accepted SASP members might not be complete and needs to be curated in a collective effort, like e. g. by the databases available. 51.
The pie chart illustrates the identified SASP factors and their response to the treatment with 1201. e Transcript levels of all upregulated SASP factors which were regulated contrariwise by treatment with 1201, displayed as Fragments Per Kilobase of transcript per Million mapped reads (FPKM). Error bars indicate confidence intervals (95%). f Six genes were quantified with RT-qPCR from the same RNA samples which were used for RNA-Seq and correlated with the transcript levels derived from RNA-Seq. From a to f, data represent the average of three experiments. Statistical significance for the treatments in a (p < 0. 001 for SIPS treatment and p = 0. 088 for 1201 treatment) and b (p < 0. 001) were calculated with a two-way ANOVA followed by a Bonferroni post hoc test. The p-values of e were corrected with the false discovery rate for multiple comparisons using the Benjamini-Hochberg method proper Use Human Tissue of the Dutch Federation of Biomedical Scientific Societies.
SIPS Cells were seeded at a cell density of 3500 cells/cm 2 one day prior to the treatment. The cells were treated nine times with 100 µM H 2 O 2 supplemented to the media for 1 h followed by a media change. Induction of premature senescence was verified by SA-β-gal staining, p21 expression, and absence of BrdU incorporation. AnnexinV/PI staining was performed to assure that the treatment was non-lethal and SIPS HDFs were cultured and monitored for over 50 days to assure that the induced growth arrest was permanent (Terlecki et al. in preparation). Next-generation sequencing and data analysis Library preparation and sequencing were performed on an Illumina HighSeq 2000 Platform (GATC Biotech AG; Konstanz, Germany). All analysis steps were done according to the Tuxedo Suite Pipeline. 52 Briefly, Illumina Casava 1.
8. 2 software was used for base calling. RNA-seq reads were aligned to hg19 genome assembly using TOPHAT Version 2. 0. 13 with default parameters. Transcripts were assembled in Cufflinks Version 2. 1. 1 and differentially expressed genes were predicted by Cuffdiff. Visualisation of data was performed in R using the CummRbund package and The Unscrambler X software (Camo). Functional clustering analysis and filtering for the keyword "secreted" (KW-0964) was done using DAVID v6. 8. Pathway analysis was realised with Ingenuity pathway analysis software (Qiagen). Pathway enrichment analysis was performed employing Consensus-PathDB, by using the overrepresentation analysis tool. We searched against pathways in all databases with a minimal overlap and a p-value cut-off of 0. 0001.
ACKNOWLEDGEMENTS We thank  Marina Wagesreither  and the  BOKU-VIBT Imaging Center  for technical support. This work was funded by the  Christian Doppler Research Association. The financial support by the  Austrian Federal Ministry of Economy, Family and Youth , the  National Foundation for Research, Technology and Development  is also gratefully acknowledged, as is funding by the  Austrian Science Fund  (FWF:  I2514  to J. G. ). B. S. is a fellow of the international PhD programme " BioToP-Biomolecular Technology of Proteins ", funded by the  Austrian Science Fund  (FWF:  W1224 ).
Bioinformatics and Statistical Analysis 16S rRNA metagenome sequencing data is available at the NCBI Sequence Read Archive (SRA) (http://www. ncbi. nlm. nih. gov/sra) under accession number PRJNA392322. Raw sequence data was processed in multiple steps using the Quantitative Insights into Microbial Ecology (QIIME) pipeline  [28]. First, the 300 bp paired-end reads were joined using the fastq-join method with minimum allowed overlap of 120 bp and 15% maximum allowed difference within region of overlap. Second, reads with more than three consecutive base calls having Phred score <20 were truncated, reads with any ambiguous base call were discarded, and reads from different samples were tagged with sample identifiers and merged into a single FASTA file. Third, sequence reads were aligned against the core reference alignments of the Greengenes database (GG13_5; greengenes.
lbl. gov) [29]  using pyNAST  [30]  and operational taxonomic units (OTUs) were identified at the 97% DNA similarity level using UCLUST  [31]. The counts of each OTU were normalized by the total number of aligned reads per sample. Sequences that failed the closed-reference alignment to the Greengenes database were aligned de novo and OTUs with <2 aligned sequences were discarded. While close-reference OTU picking detected 11,526 OTUs, 59,960 OTUs were picked de novo. We considered 64,395 OTUs associated with bacteria. Alpha and beta diversity metrics (Chao1 and UniFrac, respectively) were used to measure bacterial community composition  [32] [33] [34] [35] [36]. We used hierarchical clustering and principal coordinate analysis (PCoA) of weighted Unifrac pairwise sample dissimilarity to show the similarity in OTU abundance profiles between samples.
Acknowledgments: We thank the members of the  COBRA Grotto of the National Speleological Society  for their assistance during the caving trips. We are very grateful for the valuable suggestions from four anonymous reviewers. Funding was provided by the  Arkansas Space Grant Consortium ,  Arkansas INBRE program , with a grant from the  National Institute of General Medical Sciences, (NIGMS) ,  P20 GM103429  from the  National Institutes of Health , and  Lyon College. Galina Glazko  and  Yasir Rahmatallah  are supported in part by the  IDeA Networks of Biomedical Research Excellence (INBRE)  grant  P20RR16460. This work employed the  High Performance Computing (HPC)  resources at the  UALR Computational Research Center  that is supported by the following grants:  National Science Foundation  grants  CRI CNS-0855248 ,  EPS-0701890 ,  MRI CNS-0619069 , and  OISE-0729792.
PacBio CCS Read Generation The PacBio RSII instrument produces sequencing reads with an average read length of ~15 kb, which would theoretically pass over a 1,100 bp molecule more than 10 times, producing CCS2 sequences with an accuracy ~99. 9%. Longer read lengths can be achieved by increasing the instrument run time, so these data were collected using 15 SMRT cells and 6 h movies, to maximize read length and, thus, number of passes. Following sequencing, the raw data were processed using the CCS2 pipeline (CCS code used available at https://github. com/PacificBiosciences/unanimity). All CCS2 reads that were 99. 9% accurate or greater were exported for further analysis. The raw NGS data of the 85-related P3 sequences can be found in the NCBI Sequence Read Archive with the accession number SRP124616.
IMGt/highV-QUest Analysis of the PacBio CCs2 Reads Characterizing IMGT/HighV-QUEST scFv Reads from AAR3 The FASTQ files of the PacBio CCS2 reads were imported and converted to FASTA sequences for submission to IMGT/HighV-QUEST  5   (29, 30) , which implements IMGT/V-QUEST program version 3. 4. 2 (August 4, 2016) and IMGT/V-QUEST reference directory release 201631-4. The analysis was performed with the advanced functionality "Analysis of single chain Fragment variable (scFv)" (IMGT/HighV-QUEST Documentation, see text footnote 3). Data filtering was applied with the following criteria to be fulfilled for each of the two V domains: (i) >85% of identity of the V-REGION of the V domain with the V-REGION of the closest germline IMGT gene and allele and (ii) in-frame V-(D)-J junction. Filtered sequences were then analyzed to identify the closest V, D (for VH) and J IMGT genes and alleles, to characterize the amino acid (AA) junction, to evaluate the mutations and to give a complete description of the scFv with IMGT labels (IMGT Index > scFv  6  ).
Tracking and Analysis of Identical and Related PacBio Reads of the AAR3 scFv-Phage Clone P3 In order to evaluate the PacBio scFv read sequencing quality, reads identical or closely related to the sequence of a scFv-phage clone P3 (previously isolated from the same fraction AAR3 and Sanger sequenced) were tracked among the total reads generated in the 15 SMRT cells by two approaches. First, reads potentially related to P3 were searched for based on the expected VH-VL characteristics determined by IMGT/HighV-QUEST (same V and J genes and alleles, and same AA junctions). Second, a Blast search was performed to check whether P3-related reads could have been missed by the IMGT/HighV-QUEST filtering. The fasta headers of the sequences were modified to include the sequence set identifier and a blast database was built (formatdb 2.
2. 26) from the accumulated reads from the 15 SMRT cells. The database search was performed using the blastn program (2. 2. 26), with the combined following criteria: longest alignments on the P3 Sanger (767 bp) sequence, highest identity percentage, and maximum number of 20 mismatches or indels. Among the extracted reads only those fulfilling the P3 characteristics (in terms of V and J genes, allele names and AA junctions) were retained.
Any reads that did not reach consensus coverage of QV30 (99. 9% accurate) or above were filtered out. Those settings and filters are built into the CCS2 pipeline that is available via the PacBio web-based analysis software (SMRT Link). Thus, these 450,558 reads passed initial filtering with an average pass number of 24 and a quality score of minimum 99. 94% accuracy (Figure  3C ). Another contributing factor to data quality and throughput was the use of the longest movie lengths possible at the time (6 h) to ensure the longest read lengths for analysis.
ACKNoWLedGMeNts We would like to thank Franck Salin for his helpful advice on DNA amplification and analysis using the Agilent 2100 Bioanalyzer. We are really grateful to Alexandre Fontayne for his help in the P3 patenting process. We thank Roberto Lleras for helpful information. We are grateful to Gérard Lefranc for helpful comments, Arthur Lavoie and Karthik Kalyan for IMGT/HighV-QUEST and to all IMGT team members for their constant motivation. IMGT ® is Academic Institutional Member of the International Medical Informatics Association (IMIA) and of the Global Alliance for the Genomics and Health (GA4GH). The Supplementary Material for this article can be found online at http://www. frontiersin. org/articles/10. 3389/fimmu. 2017. 01796/ full#supplementary-material.
Database of bioassay records linked to geographical locations A previously compiled database that searched the published literature up to October 2008 was provided by its authors  [8]. We then searched the published literature from January 2009 to August 2016 using the Web of Knowledge database of published articles and the search terms "resistance" and "aedes". Abstracts were scanned and full-text copies obtained for articles of potential relevance. Additional unpublished datasets were provided by members of the WIN. Data were extracted from all articles reporting either mortality or the lethal concentration required to kill half of the sample (LC 50 values) from bioassays of Ae. aegypti and/or Ae. albopictus mosquito populations using 1 or more of the 4 major classes of neurotoxic insecticide (carbamates, organochlorines, organophosphates, and pyrethroids).
Results and discussion The geographical distribution of the insecticide-resistance phenotype A database of 6,888 bioassay records was compiled including 1,190 records from the 2008 review  [8]  and 5,698 new records. All data derived from published articles and those unpublished data for which we received permission to share are provided in S2 File, and the total number of data records for each class of insecticide resistance is given in Table  1. The geographical distributions of records of resistance/susceptibility to pyrethroids and organophosphates in Ae. aegypti and Ae. albopictus are shown in Fig 1. Good data coverage is evident for Ae. aegypti populations in much of the Americas and Asia but is lacking for Australasia and large parts of Africa. Less data is available for resistance to the main neurotoxic insecticides in Ae.
This may indicate that insecticide resistance is spatially heterogenous at a fine resolution but could also be the result of variation in test protocols, variation in collection dates, or small sample sizes. Apparent trends need to be treated with caution because these data do not come from a single study with a systematic sampling design and consistent methods. The dataset is highly biased, clustered in both time and space, and contains values generated using different methods. For Ae. aegypti, the most commonly tested organophosphate was temephos using larval diagnostic dose bioassays, and Fig  3  shows the distribution of results from these assays in each region for which data were available. No assumptions have been made about the levels of resistance that equate to "susceptible", "possible resistance", "moderate resistance", "resistant", or "highly resistant", so the ratios obtained using the Rockefeller strain for comparison were classified into quartiles.
Gaps in the insecticide-resistance phenotype data A comparison of the respective distributions of Ae. aegypti and Ae. albopictus with the full insecticide-resistance datasets available for each species over the last decade (2006-2015) highlights those locations lacking contemporary data (Fig  1 ). For Ae. aegypti, there are very large gaps in the data available in Africa and far southeastern Asia/Australasia, in contrast to insecticide resistance in malaria vector species where data are available across much of Africa  [10, 11]. Fewer data are available for Ae. albopictus and there are substantial data gaps on all continents. The maps in Fig  1  show the geographical spread of the data but combine multiple years and therefore mask any gaps in the temporal data coverage. The only region with good annual data coverage in the dataset presented here is Brazil (S2 File), although similar insecticide-resistance monitoring datasets may be held by other nations.
Development of DNA markers for key metabolic genes will facilitate screening alongside VGSC mutations in field populations whilst controlled crosses and genome editing in the laboratory should be used to segregate and study the independent effects of each mechanism. In addition to the standardisation of methods, development of diagnostics, and integration of phenotypic and genotypic data to improve monitoring, it is important that the resulting data are shared. Research data need to reach decision-making bodies, and health agency data held by individual countries need to be shared to address a critical global issue that is not restricted by national boundaries. Regular updates to online databases needs to be a key goal coupled with appropriate training of end users. A number of databases already exist for vector data, namely VectorBase, managed by the University of Notre Dame and Imperial College London, the Malaria Atlas Project, managed by the University of Oxford, and IR Mapper, managed by Vestergaard and the Kenya Medical Research Institute  [76] [77] [78].
compiling an extensive dataset for Brazil, and Poonam Sharma Velamuri and Elamathi Natarajan for their work on the data for India. We thank the following groups and individuals for contributing unpublished datasets: Goncalo Seixas and Carla Alexandra Sousa, Global Health and Tropical Medicine, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa; Beniamino Caputo and Alessandra della Torre, Istituto Pasteur-Fondazione Cenci-Bolognetti, Dipartimento di Sanità Pubblica e Malattie Infettive, Università "Sapienza", Rome, Italy; Sebastien Marcombe and Paul Brey, Institut Pasteur du Laos, Vientiane, Lao People's Democratic Republic Waraporn Juntarajumnong, Kasetsart University, Bangkok, Thailand; Ferdinand V. Salazar, Department of Medical Entomology, Research Institute of Tropical Medicine, Department of Health, Philippines; Laboratorio de Entomologia Aplicada (LEnA), Sucen, Marı ´lia, SP/ Brazil.
Cycle sequencing was performed by fluorescent Dye Terminator chemistry (BDTv3. 1, Applied Biosystems) per the manufacturer. Sequences were collected with an automatic genetic analyzer (3100 Avant, Applied Biosystems). Corrections and contig reconstitutions were made with SeqScape software (Applied Biosystems). Sequences were aligned under the CLUSTAL X program with the reference sequences representing the overall HIV-1 group M genetic diversity observed in Central Africa. We included all pure subtypes and the tentative subtype L variant. We also included all the sub-subtypes and reference strains for all the CRFs available in the databases and documented in Africa (CRF01, 02, 04, 05, 06, 09,  11, 13, 18, 19, 27, 28, 36, 37, and 45). Moreover, we also added other sequences of unique recombinants from DRC (MAL and NOGIL with Accession Numbers X04415 and AJ237565.
e. , excluding those that were not represented in this study. The clustering of each new sequence should be concordant among all the trees. To obtain more detail about recombining strains of HIV-1 circulating in the Congo, the same samples previously analyzed in the env region (V3-V5 loop) and gag (p24) were re-analyzed and compared to subtype designation in the pol gene to reflect new strains recently described. To determine whether the viruses were recombinants, several additional analyses were performed using the Simplot version 3. 5. 1 software (http://www. med. jhu. edu/ deptmed/sray). The percentage of similarity plots was revealed between the query group and selected groups of sequences by moving 350 nucleotides along the pol alignment with 20 base pair increments. The Simplot software also performed bootscanning on NJ trees using the same window and increment size and 100 replicates for each phylogeny.
The strain or sequence fragments that did not cluster with any of the known subtypes, CRFs or URFs, were submitted to a blast analysis on the Los Alamos and GenBank databases (http://www. hiv. lanl. gov/content/sequence. basis_blast/basis_blast. html) to determine whether they were related to previously described unknown fragments of other HIV-1 strains. Sequences were also analyzed for the presence of major and minor mutations in protease and reverse transcriptase genes at positions known to be associated with ARV resistance. Resistance mutations were identified using the Stanford Calibrated Population Resistance Tool  [11] , and samples were considered to be resistant if they contained one or more mutations as defined by the Bennett WHO list of transmitted resistance  [12]. The new Congolese sequences have been deposited in EMBL for pol sequences, and the accession numbers are as follows: FM164884-FM164933.
To do so we compare the sea level variability reproduced by an ensemble of ORCMs, most of them available from the Med-CORDEX database  (Ruti et al. 2015 , http://www. medcordex. eu), with several observational products in order to elucidate the best approach to configure Mediterranean ORCMs. We focus on the treatment of the Atlantic boundary conditions which has been suggested to be of paramount importance for Mediterranean sea level modelling  (Calafat et al. 2012). We analyse the seasonal and interannual variability of mean sea level as well as the regional differences. The paper is organized as follows: a review of the approaches followed to model Mediterranean sea level variability in ORCMs is presented in Sect. 2. The datasets and the models used in the comparison are described in Sect.
The limitations of the Boussinesq approximation act on the 3D mode, while nothing prevents the 2D mode to reproduce non-zero mean sea level variations. The information required at the LBCs could be provided by AOGCMs, global reanalyses or observational datasets (e. g. altimetry or sea level reconstructions). It has to be noted that if the Atlantic information is imposed, then no further correction for the Mediterranean thermal expansion is required. The ORCMs naturally simulate the differences in the thermal expansion between the Atlantic and the Mediterranean. Thus, if the Atlantic signal is realistic enough, the model will be able to simulate the effects of thermal expansion inside the basin.
Altimetry data We use the dataset from the climate change initiative (CCI) which aims to improve satellite-derived dataset on climatic time scales, for some variables considered as essential in the context of changing climate (essential climate variable, ECV). An exhaustive evaluation of the sea level product is presented in  Ablain et al. (2015). This dataset is a multi-satellite merged product that consists of time series of gridded Sea Level Anomalies (SLA, doi:10. 5270/esa-sea_level_cci- MSLA-1993_2013-v_1. 1-201412) : the SLA data are calculated after merging all the altimetry mission measurements together into monthly grids with a spatial resolution of 1/4 of degree. The data include a dynamical atmospheric correction. We call this dataset CCI-ECV.
MORCE-MED-variable Atlantic from reanalysis assimilating altimetry MORCE-MED  (Drobinski et al. 2012; Lebeaupin-Brossier et al. 2013 ) is a regional coupled model composed of the ocean model NEMOMED12  (Beuvier et al. 2012) , regional version of NEMO V3. 2, coupled to the atmospheric model WRF  (Skamarock et al. 2008 ). NEMOMED12 has an horizontal resolution ranging from 6. 5 to 8 km and 50 vertical levels. As for CNRM-RCSM4, the model SSH is relaxed toward a reference dataset in the buffer zone. For the period 2002-2008, the reference is GLORYS-1  (Ferry et al. 2010 ), a reanalysis of the global ocean circulation at a 1/4 • horizontal resolution available for this period, which assimilates SLA from satellite data. For the period 1989-2001, data come from the SSH monthly seasonal cycle of the previous simulation with a time shift of 5 months in the seasonal cycle to follow the cycle of GLORYS-1 in the near Atlantic domain, there is thus no interannual variability before 2002.