dataesr/scientific-paragraphs-categorization huggingface dataset:
This hypotheses, if true, hints that a simplified one-driver model of aerosol number concentration trends is not adequate, and future analyses of aerosol trend drivers must be able to include competing processes affecting the aerosol concentrations. A rigorous determination of which factors are actually behind the decreasing trends is left to future studies. The past trends are useful for determining the mechanisms and sources behind the aerosol number concentrations. Until the cause of the trends is better constrained, however, drawing strong conclusions about the future of aerosol number concentrations is not possible. If the anthropogenic influences, suggested by part of the datasets, are really behind these significant decreases in the particle number, and the assumption that N trends are representative of CCN trends is valid, the strong warming forcing of pollution controls suggested by  Brasseur and Roeckner (2005)  could have some support from these results.
, 2011) , and continuous support to aerosol observing infrastructures such as EUSAAR or ACTRIS in the EU. Currently, there are aerosol particle number concentration time series from 27 stations, and 14 stations with size distribution datasets in the EBAS database (http://ebas. nilu. no). Most of these measurements are ongoing, and in about 5 yr they start to be long enough to qualify for trend analyses. However, even with this increased data availability, there is and will be still a clear inadequacy in the measurement network density in many areas of the world, which limits the assessment of aerosol variability at the global scale, and makes it even harder to increase our understanding of future changes in the atmosphere. Supplementary material related to this article is available online at: http://www.
atmos-chem-phys. net/13/ 895/2013/acp-13-895-2013-supplement. pdf. Acknowledgements. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 262254 (AC-TRIS) as well as from the EU FP6 project European Supersites for Atmospheric Aerosol Research (EUSAAR) and the EU FP5 project Construction, use and delivery of an European aerosol database (CREATE). The GAW aerosol data are archived at and available from the World Data Centre for Aerosol (WDCA) located at the Norwegian Institute for Air Research (NILU). The GAW-WDCA data are hosted in the EBAS database (http://ebas. nilu. no/), an infrastructure shared with other frameworks targeting atmospheric aerosol properties, such as the European Monitoring and Evaluation Programme (EMEP) and the European Aerosols, Clouds, and Trace gases Research InfraStructure Network (ACTRIS).
The ERA-INTERIM re-analysis data were obtained from the The European Centre for Medium-Range Weather Forecasts (ECMWF). The GISS temperature trends are from http://data. giss. nasa. gov/ gistemp/maps/. MSU data are produced by Remote Sensing Systems and sponsored by the NOAA Climate and Global Change Program. Data are available at www. remss. com. The authors wish to thank the Finnish Centre for Scientific Computing (CSC) for computing resources. The support of the Finnish Center of Excellence in Physics, Chemistry, Biology and Meteorology of Atmospheric Composition and Climate change is acknowledged. We thank the International Foundation High Altitude Research Stations Jungfraujoch and Gornergrat (HFSJG), which made it possible to carry out the experiments at the High Altitude Research Station at the Jungfraujoch.
The JFJ operational measurements were supported by MeteoSwiss within the Swiss program of the Global Atmosphere Watch (GAW) of the World Meteorological Organization. The measurements at Melpitz, as part of the German Ultrafine Aerosol Network (GUAN), were co-funded by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety grant F&E 370343200 (Titles: "Erfassung der Zahl feiner und ultrafeiner Partikel in der Aussenluft" and "Trendanalysen gesundheitsgefährdender Fein-und Ultrafeinstaubfraktionen unter Nutzung der im German Ultrafine Aerosol Network (GUAN) ermittelten Immissionsdaten durch Fortführung und Interpretation der Messreihen"). W. B. thanks Birgit Wehner and Thomas Tuch for their involvement in collecting TDMPS data at Melpitz. The authors appreciate the dedication, commitment, and effort of Randolph Borys, Douglas Lowenthal, Ian McCubbin, and Peter Atkins towards the long-term measurements of aerosol concentration at Storm Peak Laboratory (SPL).
Data were downloaded with times 00:00 and 12:00 with step of 12 h. The monthly means of daily forecast accumulations of precipitation were obtained by summing both monthly values as described in http://www. ecmwf. int/products/data/archive/data faq. html. The data were re-gridded by the ECMWF dataserver to 1x1 degree resolution. Atmos. Chem. Phys. , 13, 895-916, 2013 www. atmos-chem-phys. net/13/895/2013/.
We also compared the the N trends with the trends of another instrumental temperature dataset: CRUTEM4v (Jones et al. , 2012) , which resulted in qualitatively similar results (see Supplement Fig. S1), with the main difference being that the for 1995-2010 period, the region around Continental US sites had generally no trend in the temperature records. www. atmos-chem-phys. net/13/895/2013/ Atmos. Chem. Phys. , 13, 895-916, 2013.
Apparent FRET efficiencies are reported as E PR ratios, according to (26) E PR ¼ F FRET F FRET +F D em D exc where, F FRET ¼ F A em D exc À dF A em A exc À lF D em D exc ; d is the acceptor direct excitation coefficient, and l is the donor-leakage coefficient  (26). All data analyses were performed using a home-made Labview routine (National Instruments, Austin, TX, USA). Resulting E PR histograms represent only the molecules containing a donor and an acceptor (based on a selection on the S parameter), and are displayed and fitted using Origin (Northampton, MA, USA). Buffer conditions for all measurements were 10 mM Tris (pH 8), 150 mM NaCl, 1 mM EDTA and 1 mM DTT.
The fluorescent label position (Atto647N) is represented by a red dot. (E) Schematic representation of the RNA anti-terminators hairpin (green). The complementary DNAc oligo used in smFRET experiments is represented in blue, together with the position of the donor fluorescent dye (Cy3B, green dot). The dimeric CAT domain of the anti-termination proteins is represented in grey. On the side, the regions that will be hybridized by the opener (black) and comp (red) oligos are represented as well. (LicT H207D/H269D ) (filled squares)  (19). These isotherms were analysed using the BIOEQS software  (27) , using a 2:1 protein-RNA interaction model, and the recovered binding free energies are reported as dissociation constants (K d ) values ± the uncertainties at the 67% confidence limit in the determination of the binding energies  (28).
Identification of a Putative Fruit Color Regulator In this study, we used Affymetrix GeneChip transcriptomic data from two separate fruit development and ripening time series to construct gene regulatory networks by artificial neural network (ANN) inference. ANNs have previously been shown to have strong modeling capabilities when correctly applied to the analysis of complex biological data across a range of domains and applications  (Lancashire et al. , 2010)  The ANN method works by training a model using a subset of the available data. In this investigation, we initially looked for novel fruit-ripening regulators by using 551 transcription factor genes present on an Affymetrix Tomato GeneChip and then reducing the number of genes in the data set to the 100 strongest interactions  (Lancashire et al.
, 2005 (Lancashire et al. , , 2010;; Matharoo-Ball et al. , 2007). The outcome of the ANN method was an inferred network of interactions based on these top 100 interacting genes. An initial data set, when analyzed by ANN, provided a network of transcription factors with several major highly interacting nodes, including one (Fig. 1A ; Supplemental Fig. S1 ; Supplemental Data Set S1) occupied by a gene annotated as having some similarity to the Arabidopsis GLK sequence on the GeneChip array that can be found on the tomato genome assembly (Tomato Genome Consortium, 2012) as Solyc08g077230 (GenBank accession no. KC147634). The same gene was also represented as a node with a high number of edges in a further network constructed from a much more extensive ripening time series but using the same 100 genes as before (data not shown).
Like DET1, constitutive silencing of DDB1 (hp1) also leads to elevated plastid number and size and involves the CUL4-based E3 ligase complex, suggesting a possible involvement of ubiquitin-mediated degradation of these organelles  (Wang et al. , 2008). In  the hp3 mutant, higher concentrations of carotenoids and chlorophyll were also measured in both leaves and the pericarp of green fruit. ABA deficiency in hp3 leads to an enlargement of plastid compartment size, probably by increasing plastid division, thus enabling greater biosynthesis and a higher storage capacity for the pigments  (Galpaz et al. , 2008). Our data indicate that overexpression of the tomato APRR2-Like gene is mediating its effect by both increasing the plastid size in the cells and the levels of chlorophyll and carotenoids.
An Ortholog of the Tomato APRR2-LIKE Gene Is Associated with Increased Pigmentation in Sweet Pepper Pepper (Capsicum annuum) genotypes exhibit a wide variation for color and color intensity at both the immature and mature stages of fruit development  (Thorup et al. , 2000). A pepper ortholog of the APRR2-Like gene would represent a strong candidate as a regulator of fruit color and color intensity. We have identified a likely APRR2-Like gene in sweet pepper by undertaking a BLAST search of a pepper bacterial artificial chromosome library using the tomato APRR2-Like sequence. This pepper gene (GenBank accession no. KC175445) maps to pepper chromosome 8 in a region that shows strong synteny with a region of tomato chromosome 8 containing the APRR2-Like locus (Solyc08g077230). We have several lines of evidence that this APRR2-Like gene in pepper can influence color in these fruits.
qRT-PCR Analysis qRT-PCR was performed with mRNA isolated from different tissues. Primers were designed by using Primer 3 software (http://primer3. sourceforge. net/). The probe was duel labeled by fluorescein at the 59 end and TAMARA at the 39 end. qRT-PCR, with an annealing temperature of 60°C, was performed in triplicate on a 384-well plate in the LightCycler 480 Real-Time PCR Detection System (Roche) using LightCycler Taqman Master according to the manufacturer's recommended protocol. The elongation factor was used as a control to normalize the qRT-PCR values across different samples (GenBank accession no. X14449). Primers are listed in Supplemental Table  S1.
In Silico Network Verification In silico verification of the ANN model was based on the top 100 transcription factors identified by phase 1 above being run through the network inference phase in a larger data set that considered all time points in the ripening process (n = 8, time points = MG to breaker + 7 d). In this analysis the APRR2-Like gene showed a high level of centrality determined by examination of a hierarchical network derived from the weightings generated in the phase 2 process described above. Sequence data from this article can be found in the GenBank/EMBL data libraries under accession numbers KC147634 and KC175445.
ACKNOWLEDGMENTS We thank  Peter G. Walley  for the RNA extractions for the GeneChip experiments and  Sarah Perfect  for expert help with the analysis of chloroplast area determinations in the tomato samples. Malcolm Bennett  and  Michael Wilson  provided useful discussions during the preparation of the manuscript. Christopher Gerrish  is thanked for technical help and  Laura Perez  for the cv M82 3 APRR2 fruit images in Supplemental Figure  S4.
Races and data sampling The data sets for this study were obtained from the individual race websites and from the race directors. The authors investigated the RAAM data available from 1982 to 2011. 26 he RAAM spans a distance of ∼5000 km and has a total elevation of ∼30,500 m. It is the longest, and considered the hardest, cycling endurance event in the world, starting at the Pacific coast and ending at the Atlantic coast in the United States. Solo riders have to complete the race within 12 days, so a mean speed of 17 km ⋅ h -1 is demanded, including noncycling times. Winning solo riders have completed the race in 8 days and a few hours, corresponding to a mean speed of ∼25 km ⋅ h -1. To participate in the RAAM as a solo rider, ultraendurance athletes have to pass the requirements of qualifying races.
The authors considered two of these qualifying races in order to obtain data about ages that were not available for the RAAM. The authors investigated the Furnace Creek 508 data available from 1989 to 2011the Furnace Creek 508 is a US RAAM-qualifier race that is 818 km long, with an elevation gain of over ~11,000 m. 27  The authors also analyzed the Swiss Cycling Marathon data available from 2001 to 2011 -the Swiss Cycling Marathon is the oldest and largest RAAM-qualifier race in Europe, where the participants have to cover a distance of 715 km with an overall elevation gain of ∼5580 m. 28.
Age of the participants and their cycling performances The changes over time in the ages of all participants, together with the average cycling speed in kilometers per hour for the male and female winners and all male finishers, were analyzed for both the Furnace Creek 508 (data from 1989 to 2011) and the Swiss Cycling Marathon (data from 2001 to 2011). Because the age of the participants was not available for the RAAM, the authors only considered the average cycling speed of the male and female winners and all male finishers of the RAAM (data from 1982 to 2011). Because of the small percentage of female finishers in the three races, less than 11% of the total finishers over the studied periods (Figure  1 ), the authors focused on male finishers only. The magnitudes of the gender difference were examined by calculating, as a percentage, the difference in cycling speed between male and female winners.
Age-related changes in performance In order to analyze the age-related change in ultracycling performances, the authors pooled the data of the male finishers of the Furnace Creek 508 from 1989 to 2011 and the male finishers of the Swiss Cycling Marathon from 2001 to 2011. The authors identified the following age groups: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, and $60 years. In order to limit the influence of environmental conditions over the years, the cycling performances were expressed as a percentage of the winner's speed in each year. Finally, the authors considered the performances of the best ten male cyclists per age group over time.
Limitations and implications for future studies Several variables with an influence on performance in an ultraendurance sport are known, such as weather influences  [54] [55] [56]  and adequate nutrition. 13, 52, 53, 57, 58 Unfortunately, these aspects were not considered in the present study. The most important limitation was that the age of athletes was not recorded in the RAAM data, so the authors had to consider two RAAM-qualifier races in order to investigate the age of peak performance. Other limitations were missing data regarding training  [10] [11] [12] [13] [14]  and prerace experience. 10, 12, 14, 29 onsequently, the authors could not investigate whether increased prerace experience also leads to a better performance in ultracycling races as it has been shown to do, for instance, in a Triple Iron ultratriathlon,  10  and whether this is an important reason why the mean age of winners in ultraendurance sports is older than the mean age of winners in non-ultraendurance sports.
(Krzywinski et al. 2009 ) illustrating the chromosomal position of differential interactions in the context of chromosomal compartments. The HOMER software suite was used to determine significant interactions between 100-kb genomic regions in either control or cohesin-deficient thymocytes (FDR = 0. 1; replicates pooled). Of 10,917 interactions that were significantly altered in the pooled samples, 1476 interactions changed in replicate 1 and 5004 in replicate 2 (P < 0. 05; the remaining interactions were only seen in the pooled data). Of 502 differential interactions that were shared between replicates, 278 were decreased (blue) and 224 were increased (red). All differential interactions were intra-chromosomal. Compartment assignment is indicated at the base of the interactions: black for open compartments and gray for closed compartments.
Either way, the data indicate a shift in the chromatin landscape in cohesin-deficient cells from cohesin-based to alternative interactions (Fig. 5D ). As a proxy for the scale of differential interactions with decreased and increased representation in our Hi-C data, we stratified SIMA results by compartment size. Interestingly, the reduction of cohesin-based interactions was most pronounced in compartments <1 Mb, suggesting an upper limit for cohesin-based interactions within compartments (Fig. 5E ; Supplemental Fig. S5 ). The alternative interactions detected in cohesin-deficient cells increased with compartment sizes >1 Mb, suggesting that they preferentially occur over larger distances or in larger compartments, which may have a higher degree of complexity (Fig. 5E ; Supplemental Fig. S5 ).
We found that the density of cohesin binding sites correlated with differential interactions of 100-kb regions that showed both increased and decreased interactions in cohesin-deficient thymocytes. Analysis at the local level showed that cohesin depletion reduced Hi-C reads that mapped within 10 kb of RAD21 and CTCF binding sites. Other interactions remained or even increased in cohesin-deficient cells. These alternative interactions included features of transcriptionally active and repressed chromatin. Using compartment size as a proxy for the length scale of interactions, cohesin-based interactions and the alternative interactions detected in cohesin-deficient cells appeared to differ in scale. Consistent with correlative data  (de Wit et al. 2013; Phillips-Cremins et al. 2013) , cohesin-dependent interactions within compartments were mostly confined to <1 Mb, i.
RNA-seq data analysis Raw reads for each condition and replicate were independently aligned to mouse transcript sequences (cDNA sequences from Ensembl version 66, NCBI37/mm9)  (Flicek et al. 2013)  using Bowtie version 0. 12. 8  (Langmead et al. 2009 ) with default pa-rameters. Gene expression estimates and normalized count equivalents were obtained using MMSEQ version 0. 11. 2  (Turro et al. 2011). We used the Bioconductor R package DESeq version 1. 6. 1  (Anders and Huber 2010)  to determine significantly differentially expressed genes in cohesin-deficient thymocytes versus control cells (FDR = 0. 05). Empirical gene expression dispersion values were estimated in a condition-specific fashion (method = ''percondition'') and used to fit a dispersion-mean relationship, where only the fitted values were used (sharingMode = ''fit-only'').
Hi-C data analysis Iterative error correction of Hi-C data was performed as described  (Imakaev et al. 2012 ). The HOMER Hi-C software analysis pipeline (http://biowhat. ucsd. edu/homer/interactions/) was used to determine significant interactions, differential interactions and to perform Structured Interaction Matrix Analysis (SIMA)  (Lin et al. 2012). Briefly, paired-end reads were trimmed to remove sequence following the canonical HindIII ligation junction sequence (1bp mismatch allowed to account for potential star activity). Trimmed reads were aligned independently to the mouse reference genome assembly (NCBI37/mm9) using BWA  (Li and Durbin 2009). Pairedend reads were merged and filtered to remove duplicate read pairs (''-tbp 1''), paired-end reads likely representing continuous genomic fragments or religation events (''-removePEbg''), self-ligations (''-removeSelfLigation''), and reads originating from regions with unusually high tag density (''-removeSpikes 10000 5'').
Multinomial logistic regression model We used gene features derived from ChIP-seq, RNA-seq, Hi-C, genomic sequence, and annotation to predict gene expression changes in cohesin-deficient thymocytes using a multinomial logistic regression model (nnet R package version 7. 3-1)  (Venables and Ripley 2002). Binary variables used included RAD21 (overlapping CTCF), cohesin-non-CTCF (CNC), CTCF, NIPBL, MED1, H3K4me3 and RNAP2 peak, and CpG island (CGI)  (Illingworth et al. 2010 ) overlap within the gene promoter (TSS 6 2. 5kb), paused promoter RNAP2 (stalling index $4)  (Hendrix et al. 2008) , gene body overlap with 100-kb differentially interacting (DI) regions (Up, increased; Down, decreased) as well as a randomly selected ''control'' set of interacting regions. We also included total gene length as a continuous variable.
Figure2. Cohesin binding predicts perturbed long-range interactions in cohesin-deficient thymocytes. (A) Schematic of approach for the mapping of differentially interacting regions. (B) Circos plot (Krzywinski et al. 2009 ) illustrating the chromosomal position of differential interactions in the context of chromosomal compartments. The HOMER software suite was used to determine significant interactions between 100-kb genomic regions in either control or cohesin-deficient thymocytes (FDR = 0. 1; replicates pooled). Of 10,917 interactions that were significantly altered in the pooled samples, 1476 interactions changed in replicate 1 and 5004 in replicate 2 (P < 0. 05; the remaining interactions were only seen in the pooled data). Of 502 differential interactions that were shared between replicates, 278 were decreased (blue) and 224 were increased (red).
Acknowledgments We thank  Drs. L. Game  and  M. Jones  for sequencing;  G. Dharmalingam ,  S. Khadayate ,  Drs. T. Carroll ,  C. Berthelot , and  J. Marioni  for help with data analysis; and  Drs. J. Elliott  and  M. Opanowicz  for cell sorting. This work was supported by the  Medical Research Council, UK  (A. G. F. , M. M. ); the  Wellcome Trust  (M. M. ); the  European Molecular Biology Laboratory  (A. J. F. , P. F. ); the  National Institutes of Health  (J. D. ); and the  National Human Genome Research Institute  ( HG003143  and  HG003143-06S1 ) (J. D. ). E. H. was supported by the  Fondation pour la Recherche Medicale , the  Agence Nationale de la Recherche , EpiGeneSys  FP7 no. 257082 Network of Excellence , a  European Research Council (ERC)  Advanced Investigator Award no. 250367 , and  EU FP7 SYBOSS Grant no.
Mucins are O-glycosylated glycoproteins present on the apex of all wet-surfaced epithelia with a well-defined expression pattern, which is disrupted in response to a wide range of injuries or challenges. The aim of this study was to identify mucin gene sequences of gilthead sea bream (GSB), to determine its pattern of distribution in fish tissues and to analyse their transcriptional regulation by dietary and pathogenic factors. Exhaustive search of fish mucins was done in GSB after de novo assembly of next-generation sequencing data hosted in the IATS transcriptome database (www. nutrigroup-iats. org/ seabreamdb). Six sequences, three categorized as putative membrane-bound mucins and three putative secreted-gel forming mucins, were identified. The transcriptional tissue screening revealed that Muc18 was the predominant mucin in skin, gills and stomach of GSB.