dataesr/scientific-paragraphs-categorization huggingface dataset:
[6, 7]. We estimated national ASRs by aggregating the data from the available cancer registries using a weighted average of local rates. To describe incidence and mortality time trends, we calculated the estimated annual percent change (EAPC) using the method proposed by Esteve et al. [8]. Trends in incidence and mortality and EAPCs were estimated only for 4 countries (Table  1 ). All of the EAPCs were tested for equality to zero by using the corresponding standard errors. We considered EAPCs statistically significant if the p-value 0. 05. We conducted the data analysis in Stata version 12. 1 (StataCorp)  [9]. In addition to the general methods described above, we considered information by histological subtype as coded in ICD-O-3. Unfortunately, this information was incomplete for most registries in the region and we decided to include, for this analysis, only registries which qualified for publication in CI5 volume X  [10].
This was, however, not the case among adolescents (Fig. 3a  and b ). b Incidence rates were estimated using data from regional cancer registries. Although a few countries have information on smoking prevalence over longer periods of time, comparable information on temporal variations in smoking prevalence for more than 10 years within countries in the region is scarce. A recent publication including a comparison between Uruguay (that introduced a strict tobacco control policy) and Argentina, showed a statistically significant annual decrease of 3. 3% (95% CI 2. 4-4. 1) in the prevalence of current use of tobacco in Uruguay, compared to a non-significant annual decrease of 1. 7% (95%CI À0. 8 to 2. 6) in Argentina  [17]. *The estimated annua l pe rcen t change is statistically diff eren t from zero (p<=.
Acknowledgements The authors would like to thank sincerely all of the cancer registry directors and their staff (listed in the Appendix to the Introduction of this Supplement) for their considerable efforts in collecting the data presented in this paper, together with members of the  IARC Section of Cancer Surveillance , especially  Sebastien Antoni ,  Murielle Colombet  and  Mathieu Laversanne  for their collaboration. The authors also wish to acknowledge  Dr Esther de Vries  and  Dr Marise Rebelo  for their valuable comments in reviewing earlier drafts of the manuscript.
Additionally, the paid Passport feature enables users to change their geolocalization and connect with people anywhere (blog. gotinder. com). Tinder's Facebook linking allows it to display the socalled verified profiles to reassure its users that if they have connections in common, some measure of safety is ensured. For instance, one can visit any of the Facebook profiles of common friends and in a couple of clicks find, verify, and explore a potential match's personal data and possibly make contact. This feature layers context: an offapp degree of connection to every swipe. While users may find ways to obscure their Tinder identity (e. g. , fake Facebook profiles, blurry photos, nicknames), other users may, through triangulation, identify how new ties are connected to strong and weak ties.
Users can go even further. For example, if a Tinder profile image is of interest but no additional platforms are linked, a quick mobile screenshot can be uploaded to any reverse image search and retrieve other sites where that user has posted the same image. The ways in which apps and profile pictures work, and get worked, underscore the significance of images regarding personal disclosure and data retrieval, privacy, and ethics. With Tinder, the "likes" and originality of images are not ends in themselves. Unlike other dating sites where users can see who has visited their profiles, in Tinder there is no way of knowing. Here, users can only register whether they like each other or not. Until recently, there was no way of signaling another user prior to "liking. " 3 Here, mobile images are positioned more as self-presentation and much less for selfexpression knowing that viewers are subject to the binary of either liking it or not.
These carte-de-visites differed from today's business cards by including photographic portraits. The speed of the feedback loop 4 between impression motivation to control how one is seen and impression construction itself plays a part. Following sociologist Erving  Goffman (1959) , these numerous everyday life photographs, populating the enormous dating/hook-up app databases, are, here, considered part of a learning process of self-mediation, training users in the editing, curating, and construction of a pseudo self-authenticity  (Winnicott, 1965). Among the stereotypical smiling, smoking, and drinking faces, distinctly original profiles stand out. These images confirm that the swipe logic can be played with and subverted, or even ignored. For example, Joachim Roncin's Je suis Charlie slogan was mass adopted shortly after the Charlie Hebdo shooting on 7 January 2015.
Using a published database  [29] , we confirmed all reduced transcripts as oligodendrocyte lineage-expressed, and 26 as lineage-specific. Gene ontology analysis using Ingenuity software mapped the cohort to myelin morphology, oligodendrocyte maturation, and myelination. Disease association identified hypomyelination, the MS model EAE, Pelizeus-Merzbacher leukodystrophy, and demyelination (Fig  1M ). Selective Loss of Differentiating Oligodendrocytes from the CNS of Olig1Cre:Klf6 fl/fl Embryos Transcriptional profiling of entire CNS data did not distinguish between changes in expression in living cells, versus global changes in expression due to loss of the cells that normally express  Early events in differentiation, such as Nkx2. 2 co-expression at E14. 5, occur normally in Olig1Cre:Klf6 fl/fl mice, but differentiating cells are selectively lost at subsequent timepoints (E).
Transcriptional Profiling Links Klf6 to Differentiation and Upstream gp130-Stat3 Signaling To find important molecular mechanisms by which Klf6 exerts its effects, we initially used RNA sequencing (RNA-seq) to identify a full cohort of Klf6-regulated transcripts (Figs 4 and S5, S1-S3 Tables). This work compared transcript numbers in Klf6-silenced versus NT control primary mouse OLP, and O4 + Apc + Mbp -iOL differentiated with T3 for 18 h (S5A and S5B Analysis of this dataset supported a role for Klf6 in differentiation-almost half of the genes identified as Klf6-sensitive have been implicated previously as regulating differentiation and/or morphology  (Fig 4B). In OLP, we identified the transcription factors Sox8, Sox9, and Sox21, which regulate oligodendrocyte and neural progenitor differentiation  [33] [34] [35].
In iOL, Klf6 also regulated Sox10, which is required for OLP maturation  [36]. Additionally, in OLP we   [39, 40]. RNA-seq data also suggested reciprocal interactions of Klf6 and the gp130-Stat3 pathway. We identified Klf6 regulation of the pathway components gp130, Stat-1 and -3, Jak1, and Socs2 and -3, suggesting that Klf6 exerts feedback on the pro-myelinating pathway that induces its expression (Fig 4B and S1 and S3 Tables). Gene ontology of the RNA-seq dataset also identified gp130 signaling as Klf6-regulated (Fig  4C ). Collectively, these results suggested Klf6 as a coordinator that links gp130-Stat3 signaling and differentiation. Intriguingly, RNA-seq data also suggested potential downstream effectors through which Klf6 might exert its actions. Notably, we identified Klf6 regulation in both OLP and iOL of Impα5, the major controller of transcription factor nuclear import in the adult CNS  [41, 42].
Importins are required for success of transcriptional programs, including those controlling differentiation and viability (Fig 4B  and S3  Table)  [41, 43]. Thus, this finding suggested a potential effector mechanism by which Klf6 might regulate oligodendrocyte development. We validated this dataset using qPCR screening of a gene subset in mouse OLP and Oli-neu cells. These studies confirmed that Klf6 silencing resulted in downregulation of gp130 pathway components (Stat3, gp130), and transcripts associated with differentiation (Sox10, Dbx2) and nuclear import (Impα5), and upregulation of transcripts linked to progenitor maintenance (Arx, Sox9) (Figs 4D and S5C). Interestingly, although Sox9 helps to maintain Pdgfrα expression in progenitors  [44] , our data from silencing studies in vitro showed that Klf6 inactivation (and associated increases in Sox9) was not associated with changes in OLP proliferation or cell cycle exit (see Fig  3A  and 3B ).
Analysis of Klf6 Chromatin Occupancy Identifies a gp130-Klf-Importin Signaling Axis To identify genes that are directly Klf6-controlled, we used chromatin immunoprecipitation and sequencing (ChIP-seq) to analyze Klf6 promoter occupancy, then cross-referenced the results to the RNA-seq dataset. ChIP-seq identified 914 sites of Klf6 binding to prospective control regions of annotated genes in OLP, and 753 in iOL, of which 337 were shared  (Fig 5A and S4 Table). Superimposing the ChIP-seq and RNA-seq data identified an overlapping cohort of 20 genes. Notably, these included gp130 and Impα5  (Fig 5B and S5 Table). We used this cohort as a starting point for analyses to identify the mechanism of action of Klf6 in the oligodendrocyte lineage. Notably, this work analyzed Klf6 interactions with upstream gp130-Stat3 signaling (Fig 5 ), and its interactions with potential downstream   Analysis of Klf6 regulation of gp130-Stat3 signaling in vivo supported this hypothesis.
Members of the Klf/Sp transcription factor family fulfill diverse functions, in roles that are lineage-and time-dependent  [13, 52]. Our findings improve our understanding of their contributions to CNS development and molecular mechanisms of action. Klf6 has previously been studied as a tumor suppressor in prostate, ovary, and liver  [18] , and Klf4, Klf5, and Klf6 are also implicated as regulators of viability  [13]. Our data now reveal that in the CNS, Klf6 regulates oligodendrocyte development and is driven by gp130-Stat3 signaling. This work further implicates the nuclear import protein Impα5 as an important, directly regulated Klf6 downstream effector. Interestingly, analysis of an external RNA-seq database also reveals similar expression profiles for gp130, Stat3, Klf6, and Impα5, which in that dataset all progressively decrease during oligodendrocyte differentiation  [29].
To assess proliferation, 10 μM BrdU (BD, San Diego, California) was added to cells 24 h prior to fixation. To assess apoptosis, immunohistochemistry using an antibody against cleaved caspase-3 was performed. To assess cell cycle exit, an index previously described  [56]  assessing the proportion of Ki67 -BrdU + cells to the total population of BrdU + cells was used. Cells positive for all markers were quantified in Z-series stacks collected at x20 magnification with 1 μm on the Z-axis. Cells were in at least five stacks per condition per marker per time point in each experiment by a blinded observer using ImageJ software.
3%, samples were incubated with appropriate AlexaFluor conjugated secondary antibody and diluted 1:100 in blocking buffer for 1 h at room temperature. Following rinsing, cells were mounted using DAPI Fluoromount-G (SouthernBiotech, Birminhgam, Alabama). Immunofluorescent stains for Klf6, Impα5, and Pdgfrα in embryonic and P1 tissue were accomplished via a signal amplification protocol (Perkin Elmer). Samples were examined using a Leica DMI or Zeiss LSM880 confocal microscope, and stacks were collected using 1-2 μm on the Z-axis and assembled into projections. For assessment of proliferation, mice received 10 mg/kg BrdU injection 1 h before fixation. To assess apoptosis, immunohistochemistry using an antibody against cleaved caspase-3 was performed. Cross-sectional Mbp + or NeuN + area and number of cells positive for lineage or stage-specific markers were measured in each stack using ImageJ software (NIH) by a blinded observer.
RNA Isolation and qPCR Analysis RNA was isolated from cultures using the RNeasy Mini kit (QIAGEN) and quality was assessed using a Nanodrop spectrophotometer (Wilmington, Delaware). RNA was used in a 20 μl reverse transcription reaction, using SuperScript RT-PCR kit (Invitrogen). qPCR was performed using PerfeCTa SYBR Green FastMix (Quanta Biosciences) and an Applied Biosystem 7900HT Sequence Detection PCR System. The melting curve of each sample was measured to ensure product specificity. Data were normalized to internal controls Actin/Gapdh/Rps11 and analyzed using the Pfaffl ΔΔCt method. Primers for qPCR are presented in S6 Table. BeadArray CNS (brain and spinal cord) of experimental Olig1Cre:Klf6 fl/fl mice and sex-matched Klf6 fl/fl littermate controls at postnatal day 1 (five per group per experiment) were dissected and snap frozen, total RNA was extracted using the RNeasy Mini kit (Qiagen), and quality was assessed by Nanodrop spectrophotometer.
Total RNA was converted to cDNA and subsequently to biotin-labeled cRNA using a commercially available kit (Illumina, San Diego, California) according to the manufacturer's instructions, and 1. 5 μg of cRNA was hybridized to Illumina MouseWG-6 v1. 1 expression BeadArray of 45,200 cDNA probes. Raw expression data were subjected to quantile normalization and low-intensity value filtering using a cutoff of 200 and then were log2 transformed (MatLab, MathWorks, Natick, Massachusetts). Differentially expressed genes were identified using unpaired two-tailed t test with False Discovery Rate (FDR) correction p < 0. 05. Qualifying genes were subjected to hierarchical clustering using Cluster 3. 0 software (Michael Eisen, UC Berkeley), and converted to a colorimetric format using Java Treeview (Alok Saldanha, Open Source Clustering Software).
2 M by heating at 67°C and shaking at 1300 rpm using a thermomixer (Eppendorf) overnight. DNA was then purified from the elution using phenol-chloroform-isoamylalcohol followed by an overnight ethanol precipitation at -20°C. The input and ChIP samples were sequenced by Illumina HiSeq 2000. ChIP-seq was performed with two biological replicates per condition. Read sequences were generated by analyzing images and base calling using Illumina data analysis pipeline. After QC filtering by FASTX (http://hannonlab. cshl. edu/fastx_toolkit/), only reads with a quality score Q20 in at least 90% of bases were included for analysis. Reads from both input and ChIP samples were aligned to mouse reference genome (mm9) using Bowtie2  [58]. The peaks in the ChIP sample in reference to the input sample in OLP and iOLs were called from read alignments by MACS algorithm  [59] , then annotated with gene information, including distance to TSS and locations in gene function elements (intron, exon, UTR, upstream or downstream) from genome mapping information of RefSeq transcripts.
Peaks within an interval of -15 kb to +5 kb to TSS of annotated genes were compared, and peaks with a maximum gap of 500 bp were considered overlapped. Alignment and coverage of ChIP-Seq data were visualized using Integrative Genomics Viewer (IGV) software  [60] , after input subtraction using methods described previously  [61]. FDRadjusted p-values of <0. 05 were deemed significant. Data are available on the GEO website (http://www. ncbi. nlm. nih. gov/geo/) (Accession number GSE79245).
RNA-seq Analysis 250ng of total RNA per sample were isolated from duplicate independent cultures of OLP, and early iOL after 18h of differentiation using the RNeasy Mini kit (QIAGEN). Libraries were constructed using the TruSeq RNA Sample Prep Kit (Illumina), and sequenced using an Illumina HiSeq 2000. Output files were subjected to quality control using FASTQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). All reported metrics including read quality score, adapter sequence contamination, and nucleotide frequency bias passed the suggested thresholds for all samples. Reads were analyzed using the Tuxedo suite  [62] , using the standard suggested pipeline with default parameters. Adapter-trimmed reads were mapped to the mouse genome (mm9) using Tophat. Transcript quantification was performed using CuffLinks  [63] , with expression normalized using CuffNorm and differential gene expression assessed with CuffDiff.
Data are available on the GEO website (http://www. ncbi. nlm. nih. gov/geo/) (Accession number GSE79245). (XLSX) S3 Table. RNA-seq profiling of Klf6-regulated transcripts shared by OLP and iOL. Findings here extend and complement results shown in Figs 4 and S5 and in S1 and S2 Tables. RNA-seq profiling data from primary mouse OLP and iOL silenced for Klf6 versus non-targeting control to identify Klf6-regulated transcripts, as described in the text and illustrated in S5 Fig. Results  from two separate cultures per condition have been filtered for statistically significant differential expression with FDR correction p < 0. 05 (Q value). Full datasets from OLP and iOL are in S1 and S2 Tables, respectively. Transcripts shared by both OLP and iOL are shown here. This analysis identifies 40 Klf6-regulated transcripts shared by both OLP and iOL.
Impα5 significantly increased the proportion of cells expressing the differentiation marker O4. Recombinant Impα5 also partially rescued differentiation in Klf6-deficient cultures. Numbers in panels (G,H) refer to proportion of cells positive for the maturation marker per field at 20x magnification, for at least four fields per condition. Data are mean ± SEM. Statistics, (B,E,G) Student's t test, (H) ANOVA plus Bonferroni post-test, *p < 0. 05, ** p <0. 01, *** p <0. 001. Scalebars, (C,G,H) 20 μm. Data are representative of at least three independent studies. ChIP-seq data are available on the GEO website (http://www. ncbi. nlm. nih. gov/geo/) (Accession number GSE79245) and in S4 Table. Individual values for all other quantifications are in S1 Data. doi:10. 1371/journal. pbio. 1002467. g006 Impα5 silencing resulted in strong inhibition of differentiation, as demonstrated by reduced expression of myelin and maturation markers (Fig 6E-6G).
Data from two separate cultures have been filtered for statistically significant differential expression with FDR correction p < 0. 05 (Q value, shown). Results from iOL are in S2 Table. Transcripts shared by both OLP and iOL are also shown in S3 Table. This analysis identifies 172 unique Klf6-regulated transcripts in OLP. Regulated transcripts are presented together with FPKM values, fold change, Q value, Unigene ID identifiers and PMID numbers for relevant references, and associated functions (summarized in Fig 4B). Results are color-coded using the scheme illustrated in Figs 4-6 for primary OLP (red) and shared transcripts (purple). Data are available on the GEO website (http://www. ncbi. nlm. nih. gov/geo/) (Accession number GSE79245). (XLSX) S2 Table.
hhmi. org/). LA and JL are recipients of  National MS Society (NMSS) Postdoctoral Fellowship Awards  (http://www. nationalmssociety. org/). This study was also supported by  NMSS  Research Grants  RG4127  and  RG5024  (GRJ),  RG1001K11  and  CA1022A1  (CSR), and the  Beker Foundation ,  Noto Foundation , and  Guthy-Jackson Charitable Foundation  (http://www. guthyjacksonfoundation. org/) (GRJ). The  ISMMS Microscopy Facility  is supported by  National Institutes of Health/National Cancer Institute  (http://www. cancer. gov/)award  R24CA095823. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
recombinant Impα5 significantly increased the proportion of cells expressing the differentiation marker O4. Recombinant Impα5 also partially rescued differentiation in Klf6-deficient cultures. Numbers in panels (G,H) refer to proportion of cells positive for the maturation marker per field at 20x magnification, for at least four fields per condition. Data are mean ± SEM. Statistics, (B,E,G) Student's t test, (H) ANOVA plus Bonferroni post-test, *p < 0. 05, ** p <0. 01, *** p <0. 001. Scalebars, (C,G,H) 20 μm. Data are representative of at least three independent studies. ChIP-seq data are available on the GEO website (http://www. ncbi. nlm. nih. gov/geo/) (Accession number GSE79245) and in S4 Table. Individual values for all other quantifications are in S1 Data. doi:10. 1371/journal. pbio.
The study was undertaken in the period from August 2013-February 2014. Questionnaires were completed face-to face by the patient or the researcher based on patient preference. The survey was ethically approved by the Faculty of Science, Engineering and Computing ethics committee at Kingston University. Data collected from the self-completion questionnaires were entered and analysed using Excel. Descriptive statistics were used to analyze the closed questions of the perception questionnaires (Table  1  & 2).
This study is concerned with the intrinsic temporal scales of the variability in the surface solar irradiance (SSI). The data consist of decennial time series of daily means of the SSI obtained from high-quality measurements of the broadband solar radiation impinging on a horizontal plane at ground level, issued from different Baseline Surface Radiation Network (BSRN) ground stations around the world. First, embedded oscillations sorted in terms of increasing timescales of the data are extracted by empirical mode decomposition (EMD). Next, Hilbert spectral analysis is applied to obtain an amplitude-modulation-frequencymodulation (AM-FM) representation of the data. The timevarying nature of the characteristic timescales of variability, along with the variations in the signal intensity, are thus revealed.
, 2013; Lauret et al. , 2016) , or have focused exclusively on a few intermediate scales  (Coskun et al. , 2011; Medvigy and Beaulieu, 2012). Although considerably differ-Published by Copernicus Publications on behalf of the European Geosciences Union & the American Geophysical Union. ing in methods, taken together the previously cited studies add valuable contributions to our knowledge of the SSI. But is it possible to analyze the variability in the SSI across multiple timescales in a unitary way? To do so, first a decomposition of the time series into uncorrelated sub-constituents with distinct characteristic timescales should be preferred. Analysis would then ensue in a like manner for each scale. The timescales, or characteristic periods of a time series, can be identified with the inverse of the frequency at which the processes that generate the data occur.
It then follows that methods portraying the changes of the spectral content of a time series with respect to time are potentially good candidates. This would enable both the identification of the periodicities and of the dynamic evolution of the processes generating the data. A general class of useful signal processing techniques can thus be identified in the so-called time-frequency distributions that depict the intensity (or energy) of a signal in the time and the frequency domains simultaneously  (Cohen, 1989). Such methods are commonly employed for geophysical signal processing  (Tary et al. , 2014). Another factor to be taken into account is the nonlinear and non-stationary characteristics of the measured solar radiation data  (Zeng et al. , 2013). Handling such data issued from the nonlinear interaction of physical processes, often also found under the influence of non-stationary external forcings, calls for an adaptive data analysis approach  (Wu et al.
, 2011). The study at hand will make use of the Hilbert-Huang transform (HHT), an adaptive, data-driven analysis technique designed specifically for investigating nonlinear and nonstationary data  (Huang et al. , 1998). The HHT adaptively decomposes any dataset into basis functions that are derived solely from the local properties of the time series. A time-frequency-energy representation of the data is then constructed from these basis functions. The HHT has seen extensive use in geophysical signal analysis and spectral estimation  (Solé et al. , 2007; Huang and Wu, 2008; Vecchio et al. , 2010; Lee and Ouarda, 2011; Alberti et al. , 2014; Huang and Shen, 2014; Tary et al. , 2014). The HHT has also been previously employed on SSI datasets  (Duffy, 2004; Calif et al. , 2013; Bengulescu et al.